AI workflows are undergoing a massive shift: moving from "can we get this model to run?" to "can we reliably deploy it to production?" Today, developers face challenges that extend far beyond model selection and inference code. Enterprise engineering teams must now piece together a sustainable pipeline that seamlessly integrates data processing, model training, inference serving, Agent orchestration, runtime monitoring, and security controls.

This shifting landscape is exactly why AI GPU computing is back in the spotlight. When most AI projects stall between the demo and production phases, the issue is rarely whether the model can be called. The real hurdles are compute stability, environment replication, inference cost management, and service reliability under high concurrency. GPUs have evolved past being mere hardware accelerators for training large models, they now dictate training cycles, inference latency, and maximum concurrent capacity, directly shaping the cost structure of enterprise AI at scale.

Why Do AI Workflows Depend More on GPU Computing Now?

Traditional cloud computing primarily serves general-purpose workloads like websites, databases, CRMs, and API gateways, relying heavily on CPUs for business logic and request scheduling. AI workloads are fundamentally different. Their core computation shifts toward matrix multiplication, tensor operations, attention mechanisms, embedding generation, and multimodal data processing. While GPUs may assist with vector-related tasks in massive vector computing or accelerated retrieval scenarios, not all vector databases and Approximate Nearest Neighbor (ANN) search implementations inherently require GPU acceleration.

This shift is driving the architecture upgrade of AI data centers. These facilities are built around GPUs or specialized AI accelerators, requiring low-latency, high-bandwidth interconnect networks, high-throughput storage, and tight synergy between compute, networking, and storage. In short, bottlenecks in AI workflows are rarely isolated code issues—they are systemic architectural challenges.

Throughout the AI lifecycle, different stages place starkly different demands on GPU compute architecture:

• Model Training: Highly sensitive to GPU VRAM capacity, compute throughput, multi-GPU interconnect bandwidth (such as NVLink), and long-term stability under heavy workloads.
• Model Inference: Prioritizes low latency, high concurrency, throughput per second (Tokens/s), and cost efficiency per request.
• Agent Deployment: Demands "always-on" continuous availability, permission isolation, high availability for tool-calling interfaces, and robust retry mechanisms.

What Role Does AI GPU Computing Play in the Workflow?

GPUs are uniquely suited for AI because the core computations of modern neural networks are inherently parallelizable. Operations like matrix multiplication, convolutions, attention calculations, and gradient updates during training can be broken down into millions of similar, smaller computational tasks executed simultaneously. While CPUs excel at complex control logic, GPUs are built for high-throughput parallel execution.

However, when selecting GPU resources for production environments, enterprises cannot look at the theoretical raw performance of a single card in isolation. Practical production environments require evaluating whether the model fits comfortably within VRAM, whether multi-GPU communication is fast enough, whether storage can continuously feed data without bottlenecks, and whether containers, drivers, CUDA versions, and frameworks are fully compatible. Teams must also consider if the inference service scales dynamically with traffic, and whether task states, logs, access permissions, and billing remain manageable.

Furthermore, high-density GPU systems are also redefining infrastructure requirements. Using GB200 NVL72 discussed in modern GPU cooling design as an example, a single rack can integrate 72 Blackwell GPUs and 36 Grace CPUs. Each GPU consumes more than 1,000 watts at peak load and uses a hybrid cooling architecture led by cold-plate liquid cooling with air cooling as support. In some public specifications, liquid cooling accounts for about 115 kW of an approximately 132 kW rack power profile, while air cooling accounts for about 17 kW. This more accurately reflects the power, cooling, and rack engineering capabilities required by high-density AI GPU systems.

How Does GPU Computing Optimize the AI Lifecycle?

1. Development & Prototype Validation: Flexible GPU Virtual Machines

At this stage, teams rarely need massive GPU clusters. The priorities are fast startup times, environment consistency, and strict cost control. By leveraging GPU Virtual Machine (VM) instances, developers can quickly spin up environments pre-configured with PyTorch, TensorFlow, CUDA, JupyterLab, or common inference frameworks. This allows teams to rapidly test data pipelines, RAG architectures, model APIs, and Agent logic while eliminating local environment discrepancies, helping enterprises validate AI concepts faster.

2. Model Training & Fine-Tuning: Scalable Distributed Tasks

As models grow larger and datasets expand, the demands on VRAM capacity, compute throughput, network interconnects, and storage I/O scale exponentially. The value of dedicated distributed training task management lies in transforming experimental code into a structured engineering workflow. It automates job submission, resource allocation, health monitoring, log tracking, access control, and cost governance minimizing hidden expenses caused by training failures, environment drift, and idle compute resources.

3. Inference & Model Serving: Highly Elastic Serverless Endpoints

Once a model enters the serving phase, the objective shifts toward achieving low latency, high concurrency, stable APIs, hot-swappable models, and predictable per-call costs. Utilizing Model Serverless Endpoints allows developers to consume text generation, image generation, and visual comprehension models directly via APIs. Compared to building and maintaining an inference cluster from scratch, this approach is ideal for rapid feature validation and scales seamlessly alongside business growth.

4. Agent Deployment & Orchestration: High-Availability Cloud Runtimes

During Agent deployment, the infrastructure needs to support more than model calls. It also needs tool connections, access control, log tracing, and background execution. It is worth noting that the base deployment of Agent orchestration or cloud runtime environments such as OpenClaw usually does not require a GPU. A stable CPU VM can handle always-on operation, tool integration, and background tasks. The value of GPUs is more often reflected in the underlying model inference, embedding generation, or multimodal workloads. Relying on a local laptop to run Agents over the long term can create risks such as shutdown interruptions, complex environment configuration, and API key exposure. With OpenClaw cloud deployment, teams can place cloud instances, model configuration, communication tool integration, and background execution in a more stable environment.

What Architecture Should Different AI Workloads Choose?

Different stages of an AI workflow should not use the same resource strategy. Development validation is well suited for GPU virtual machines; high-performance training or dedicated inference is better suited for bare metal; reproducible deployment is better suited for container services; rapid inference integration is better suited for an AI model library and Serverless Models; large-scale training is better suited for distributed training jobs; and business automation is better suited for an AI Agent platform.

This layered selection is more important than simply pursuing the highest-spec GPU. What enterprises truly need is an upgrade path that can move step by step from prototype to training, inference, and Agent automation. In the early stage, teams can validate model and business assumptions with lighter resources. After entering production, they can gradually introduce dedicated GPUs, containerized deployment, distributed training, and Agent workflows.

How Does Bitdeer AI Cloud Support the End-to-End AI Workflow?

Bitdeer AI Cloud is structured around the complete AI lifecycle. It supports development through deployment and provides two major capability groups: GPU Cloud Services and AI Studio & AI Solutions.

At the infrastructure layer, Bitdeer AI Cloud provides virtual machines, bare metal, and container services. At the training layer, it provides distributed training job capabilities to help teams manage training jobs, monitor status, and review logs. At the inference layer, Model Studio and Serverless Models allow developers to call models through APIs. At the application layer, the AI Agent platform further connects model capabilities to enterprise tools and business processes.

For teams tackling cutting-edge workloads, Bitdeer AI Cloud provides high-performance infrastructure featuring NVIDIA GB200 NVL72 clusters. These systems are purpose-built to handle massive distributed training, real-time inference, multi-agent AI ecosystems, high-performance computing (HPC) simulations, and large-scale data analytics. For engineering teams requiring ultra-dense GPU deployments, ultra-low latency networking, and advanced liquid-cooling infrastructure, these clusters deliver the stability, power, and efficiency needed for production-grade AI applications.

Conclusion

The meaning of AI GPU computing has expanded from accelerating model training to supporting the full AI lifecycle. From development validation to training and fine-tuning, from inference services to Agent deployment, and then to monitoring, scaling, and cost optimization, GPU cloud platforms are increasingly determining whether AI projects can truly enter production.

The next stage of AI competition will not be only a competition in model capability. It will also be a competition in infrastructure efficiency. Enterprises need more than larger GPUs. They need a workflow architecture that allows compute, models, deployment, and Agents to scale together.

Bitdeer AI Cloud serves as the unified gateway for this architectural transition. It enables your teams to start lean with prototype development and smoothly transition into sustainable, production-grade AI workflows, turning isolated experiments into resilient, revenue-generating business systems.

When an AI agent gives a wrong answer, it’s easy to blame the model's reasoning. But in production agentic systems, the failure often happens one step earlier—with retrieval. If the retrieval layer misses the right passage, code file, policy, or customer-specific record, the downstream agent starts from the wrong context and no amount of reasoning power can recover from a bad starting point.

And agents retrieve constantly. They decompose tasks into multiple queries, rewrite queries until the right data surfaces, search memory, and inspect code often dozens of times within a single task. Every weak retrieval adds turns, tokens, latency, and hallucination risk. As enterprises move from simple semantic search to retrieval-driven AI systems, retrieval quality has become the control point for accuracy, cost, and trust.

Today, NVIDIA Nemotron 3 Embed 8B and NVIDIA Nemotron 3 Embed 1B are available on Bitdeer AI Model Studio, bringing production-ready open embedding models for RAG, enterprise search, code retrieval, and agentic workflows. You can immediately deploy the models through Bitdeer AI Model Studio’s serverless inference platform, built for secure, scalable enterprise AI.

What is NVIDIA Nemotron 3 Embed?

Nemotron 3 Embed is a collection of open embedding models designed for enterprise and ISV teams building retrieval systems for enterprise search and emerging agentic workflows. The launch pairs two models on a single, practical accuracy-efficiency curve:

• Nemotron 3 Embed 8B — a frontier-quality embedding model designed to establish the accuracy ceiling across major retrieval benchmarks. The model topped the RTEB leaderboard overall across both open and closed embedding models.
• Nemotron 3 Embed 1B — an efficient embedding model designed to retain more than 95% of the 8B model's accuracy through pruning, distillation, and quantization-aware training (QAT), built for high-volume production workloads that need strong accuracy with substantially better throughput and serving efficiency.

The design philosophy: use the 8B model where maximum retrieval quality matters, and the 1B model where high-volume workloads demand efficiency. Together, they let teams improve retrieval quality without impractical compute tradeoffs.

Key Specifications

Why Retrieval Quality Now Decides Agent Quality

Most enterprise retrieval stacks face a set of hard tradeoffs:

• Accuracy vs. cost. Better retrieval usually means paying more per query.
• Accuracy vs. model size. Larger embedding models can improve retrieval quality, but they are harder to serve at scale.
• Latency vs. recall. Production systems need fast query response, but high-quality retrieval often means searching large volumes of content.
• Openness vs. production readiness. Open models can be customized, but teams still need tested deployment paths, quantization support, and enterprise-grade serving.

This tradeoff is most visible in agentic systems. Multi-turn agents retrieve repeatedly for planning, memory, code, and tool-use context. Weak retrieval increases turn count, token usage, hallucination risk, and user frustration. Strong retrieval reduces irrelevant context and keeps agents grounded. For retrieval to become a default agent capability, embedding models need to feel almost as easy to use as file search: fast, inexpensive, accurate, and available without major compute tradeoffs.

Nemotron 3 Embed addresses these tradeoffs head-on:

Frontier accuracy for retrieval agents. Nemotron 3 Embed models deliver frontier retrieval accuracy across enterprise search, RAG, code retrieval, and agentic retrieval workflows. Nemotron 3 Embed 8B tops the RTEB leaderboard over other open and closed embedding models, and is designed to achieve the highest agentic retrieval accuracy with the fewest tokens used among open embedding models. 

Efficiency without impractical compute tradeoffs. Nemotron 3 Embed 1B retains more than 95% of the 8B model's accuracy through pruning, distillation, and quantization-aware training, and NVFP4 support on NVIDIA Blackwell doubles throughput. This makes semantic search fast and inexpensive enough for agents to retrieve constantly, almost like file search. On NVIDIA Blackwell GPUs, the NVFP4 version delivers 2x higher throughput while retaining 99% of BF16 accuracy.

Open and transparent, from weights to recipes. Nemotron 3 Embed ships with open model weights, datasets, training recipes, optimization techniques, and fine-tuning guidance. Teams can inspect how the model was built, fine-tune it for domain-specific retrieval, and deploy it with full control over their data and infrastructure: no black box, no lock-in. 

Enterprise Use Cases

Agentic retrieval. Give agents a stronger retrieval layer for multi-turn planning, tool use, memory, and repeated lookup loops. Nemotron 3 Embed supports query decomposition where an agent breaks one user request into multiple retrieval queries and query rewriting, where an agent reformulates a query multiple times until the right data is retrieved.

RAG and enterprise search. Ground copilots and RAG pipelines in the right enterprise context documents, knowledge bases, and policies with frontier retrieval accuracy that reduces irrelevant context and wasted tokens downstream.

Code retrieval. Retrieve relevant source files, functions, and implementation examples for developer copilots and software engineering agents. With text and code embedding in one model, engineering teams can power code search and coding-agent context from a single retrieval layer.

Run Nemotron 3 Embed via API on Bitdeer AI Model Studio

You can run Nemotron 3 Embed on Bitdeer AI Model Studio, our serverless inference platform designed to make access to advanced foundation models simple and scalable. With a unified API, Model Studio allows developers and enterprises to start using models quickly without managing underlying infrastructure, reducing deployment complexity and time to value.

Bitdeer AI is a preferred NVIDIA Cloud Partner, certified to ISO/IEC 27001:2022 and SOC2 Type I & Type II, providing the secure, compliant, high-performance, and enterprise-grade infrastructure that production retrieval and agentic AI deployments require. Run your embedding workloads at the precision and scale your business requires, on Bitdeer AI's purpose-built GPU fleet.

Get Started

1. Log in to Bitdeer AI Model Studio
2. Locate NVIDIA Nemotron 3 Embed 8B or NVIDIA Nemotron 3 Embed 1B in the model list

3. Generate an API key and start making embedding API calls

NVIDIA Nemotron 3 Embed 1B:

curl -v --location 'https://api-inference.bitdeer.ai/v1/embeddings' --data '{"model":"nvidia/Nemotron-3-Embed-1B-BF16","input":"The cat danced gracefully under the moonlight, its shadow twirling like a silent partner."}' --header 'Authorization: Bearer <API_KEY>'

NVIDIA Nemotron 3 Embed 8B

curl -v --location 'https://api-inference.bitdeer.ai/v1/embeddings' --data '{"model":"nvidia/Nemotron-3-Embed-8B-BF16","input":"The cat danced gracefully under the moonlight, its shadow twirling like a silent partner."}' --header 'Authorization: Bearer <API_KEY>'

Conclusion

Retrieval is becoming the control point of enterprise AI,  the layer that decides whether agents stay grounded or go off course, and whether every reasoning cycle is spent on the right context or wasted on the wrong one. NVIDIA Nemotron 3 Embed brings frontier retrieval accuracy to practical enterprise deployment: an 8B model that sets the accuracy ceiling, a 1B model that carries that quality into high-volume production, and full openness from weights to recipes. With Day-0 availability on Bitdeer AI Model Studio, you can start building higher-quality RAG, search, and agentic retrieval systems today with better context, fewer wasted tokens, and more grounded AI systems.

Authors: Eric Si, Evelyn Xiong

The Two Kinds of AI Nobody Talks About Together

AI has already made several decisions on your behalf today, most of them without you noticing. The sequence your feed showed you. Whether a transaction cleared. None of those involved a chat interface. They operated as embedded decision systems, integrated into existing infrastructure, acting in real time. 

Then there is the AI that has become visible: a text box waiting for a question, a tool you interact with deliberately. Both categories are real and both create value. But they represent fundamentally different levels of complexity, risk, and organizational readiness to build. 

Most public conversation focuses on the second type. Most durable enterprise value is being built with the first. 

Five Technologies Worth Understanding Right Now

The AI landscape is expanding faster than most organizations can track. New model architectures, deployment patterns, and tooling categories emerge constantly, and the full list of technologies reshaping business operations goes well beyond what any single piece can cover. What follows are five capabilities I consider foundational to serious enterprise AI deployments right now. They are not the only ones worth understanding, not by a wide margin, but they are the ones I come back to most when thinking about what it actually takes to move from prototype to production. Think of them as sequential building blocks: each one extends the reach of the last.

Generative AI. This is where most enterprise deployments begin. It produces text, code, summaries, and structured content on demand. In practice it almost always means a working draft that a person reviews before anything goes anywhere. The human review step is not a limitation to be removed. It is the right design for high-stakes output, and organizations that treat it as temporary tend to learn why it was there. 

Retrieval-Augmented Generation (RAG). General-purpose models are trained on general knowledge. They have no awareness of your organization's policies, products, or procedures. RAG solves this by retrieving relevant documents at inference time rather than relying on what was baked into training. The difference between a system that fabricates a plausible answer and one that cites the correct policy is not about which model you licensed. It is a data architecture decision. 

AI Agents. An agent shifts AI from answering to acting. Given a goal and available tools, it plans and executes through to completion. A system that describes available flights is not an agent. A system that searches availability, checks your calendar, completes the booking, and sends a confirmation is an agent. The difference is architectural, not cosmetic.

Multimodal AI. When AI can work across images, scanned documents, audio, and video, entirely new workflows become automatable. An invoice arrives as a photograph. The system reads it, extracts line items, matches them to budget codes, and routes to the right approver with no manual data entry. That removed step is where operational leverage compounds.

AI Governance. Governance is undervalued early and recognized as essential after the first serious incident. The controls, audit mechanisms, and accountability structures governing an AI system are not friction on deployment. They are what makes it possible to grant AI more authority over time without increasing risk. Organizations that treat governance as optional tend to find out why it is not. Each successive capability gives the system more autonomy to act. Governance is what earns the right to grant it.

What Is Actually Inside an AI System

When an AI system fails, the instinct is to blame the model: buy a better one or switch vendors. That is the wrong diagnosis most of the time, and an expensive one to act on. A production AI system has at least five distinct layers, and failure can come from any of them.

Interface layer: What users see. It is designed to appear simple, which means everything complex is hidden behind it. Apparent simplicity is not evidence of underlying simplicity. 

Orchestration layer: The decision engine behind every interaction. It determines whether a message needs a search, whether prior context is relevant, or whether a tool should be called. Invisible in normal use, but it governs how the entire system behaves. 

Knowledge layer: Where the system draws its understanding of your organization. A sophisticated model working from outdated or poorly structured data will consistently produce poor outputs. This is the most common source of failure in enterprise AI, and reliably the last place teams look. 

Tools layer: Without tools, AI can only advise. With tools, it sends messages, updates records, and triggers downstream systems. Agentic capability lives here, as do the consequences when something misfires. 

Human oversight layer: Where accountability is defined and enforced. For routine tasks, AI drafts and a human approves. For consequential decisions involving medical, financial, or legal exposure, a person must sign off before any action is taken. A well-designed system treats this layer as mandatory. When something goes wrong, the right question is not which model to replace. It is which layer broke, and why.

The Progression from Assistant to Agent

AI deployments vary widely in capability and in the consequences of failure. Four stages help frame the difference, because the risk profile escalates meaningfully at each step.

Stage one: AI assistant. Responds when asked. No autonomy, nothing happens without a prompt. This is where most organizations start, and where many stay.

Stage two: AI workflow. Activates when an event occurs, not when a user asks. An invoice arrives; the system extracts data, classifies the expense, checks it against budget, and routes it for approval. Humans are involved at decision points, not every step.

Stage three: AI agent. Receives a goal and determines its own path. Identify strong candidates, review backgrounds, draft outreach, flag anything worth a second look. The objective is given once. The agent works out the steps.

Stage four: multi-agent system. Multiple specialized agents working in coordination: research, compliance, handoffs, oversight, planning. This architecture scales in ways a single-agent system cannot, and it is already in production in certain industries.

The distance between stages carries real consequences. A flawed summary costs minutes to fix. An agent that sends erroneous communications to thousands of customers, misroutes a payment, or cancels a booking without authorization is not making an error. It is creating a business incident with financial, legal, and reputational impact. 

Before deploying at stage three or four, any organization should answer five questions honestly: Is the system's reasoning reliable enough for this specific task? What sensitive data will it access, and does it need all of it? Which systems can it reach, and should it reach them? Which decisions require human approval before action? And if something goes wrong, is the audit trail sufficient to reconstruct what happened? These are not compliance formalities. They are the engineering questions that separate a production-grade system from a well-dressed prototype.

The Skills That Compound Over Time

When AI can produce competent output at near-zero marginal cost, routine output loses scarcity value. Judgment gains it: domain expertise, contextual reasoning, the ability to take responsibility for a decision. Those are precisely what AI does not supply. 

The professionals positioned well in AI-augmented organizations are not those who have adopted the most tools. They are those who understand their domain well enough to recognize when AI output is wrong. Eight skills tend to compound in this environment:

Problem decomposition. Taking an ambiguous situation and breaking it into steps a system can reliably follow. This matters at every level of AI system design, from how instructions are written to how workflows are sequenced.

Workflow design. Knowing which steps to automate, which to augment, and which to keep under direct human control. Where humans stay in the loop is a design decision with real consequences, not a default to leave unexamined.

Critical evaluation. Treating AI output as a starting point, not a conclusion. Every serious AI platform includes an accuracy caveat for good reason. Professionals who internalize it hold an edge over those who do not.

Data literacy. Understanding where information comes from, how it was structured, and when it becomes unreliable. The quality ceiling of any AI system is set by its data, regardless of how capable the model is.

Risk awareness. Thinking through failure modes before they occur. Practitioners with operational AI experience develop intuition for where gaps tend to appear, and that intuition has real market value.

Cross-functional communication. Making complex systems legible to people who did not build them. Every AI system eventually needs to be governed or operated by someone outside the technical team.

Governance thinking. Asking not only what a system can do, but who is accountable when it errs, what data it should access, and whether access controls are actually calibrated to the real risk. 

Continuous learning. The tools and models in use today will look different within two years. Frameworks for understanding systems carry forward. Fluency with any particular tool's interface does not.

The Scarcest Resource in AI Deployment

What remains scarce is not only infrastructure, such as GPU compute, but also the judgment to design AI systems that hold up in production: knowing where to place intelligence within a workflow, where to keep humans accountable, what failure looks like before it happens, and how to build something that performs inside a real organization rather than only in a controlled demonstration. 

To bridge this gap, Bitdeer AI provides tiered resource services for complex AI workflows. The bottom layer is compute, where GPU clusters and data centers form the foundation for all upper-layer functionalities to operate. The middle layer is the AI Cloud, enabling enterprises to host and scale models without the need to manage their own hardware. The top layer is the model application layer, where AI applications connect to live systems and execute multi-step processes.

The shift from AI as a standalone tool to AI as an integrated component of governed, production-grade systems is underway across every sector. Organizations that build strong system design practices early will hold an advantage that is difficult to replicate. 

AI development is no longer limited by model performance alone. For developers and technical leaders, the real production challenge is whether an idea can move from a notebook or small-scale experiment into a secure, repeatable, and scalable GPU cloud environment. Large language model (LLM) training, fine-tuning, Retrieval-Augmented Generation (RAG), batch inference, and low-latency model serving each place distinct demands on VRAM, storage throughput, networking fabric, runtime consistency, access control, and cost visibility. When these infrastructure elements are an afterthought, teams encounter volatile GPU utilization, sluggish deployment cycles, and spiraling operational costs as workloads scale.

At Bitdeer AI, we see the bottleneck as infrastructure orchestration: acquiring suitable GPUs infrastructure, maintain high-speed cluster interconnectivity, continuously feed accelerators via high-throughput storage, manage deterministic runtime environments, and deliver predictable compute for workloads that may scale from a single prototype to distributed production. Our mission is to simplify the infrastructure layer, allowing teams to spend less time managing compute environments and more time building core AI applications.

Why AI GPU Infrastructure Became Harder to Operate

Taking AI workloads to production requires far more than raw compute power. It requires a coordinated system of GPU capacity, CPU orchestration, memory bandwidth, storage throughput, network performance, scheduling discipline, security, and operational control. The gap between a local proof of concept (PoC) and a production AI cloud environment often becomes visible only when teams begin to scale.

GPU Cloud Availability vs. Workload Matching

The primary challenge is not just sourcing GPUs, but precisely matching accelerator configurations to specific workloads. Training, fine-tuning, batch inference, and real-time serving have wildly different requirements for VRAM capacity, throughput, latency, and utilization. Choosing infrastructure before defining workload patterns often forces teams to pay a premium for idle capacity or face unstable delivery due to under-provisioned compute.

Cluster Networking and Data Flow

Modern AI rarely runs as an isolated single-card task. Distributed training relies heavily on fast parameter exchange (via efficient cluster interconnects), fast checkpointing, and storage systems capable of continuously feeding the GPUs. Conversely, inference serving requires orchestrating request routing, dynamic batching, and model serving without letting the network become a bottleneck. The core challenge is not just owning GPUs, but making multiple components operate seamlessly as a unified compute system.

Power Density, Cooling, and Reliability

AI infrastructure also changes the power equation. High-density GPU clusters cannot be planned as if power delivery, cooling, and AI data center design are background concerns.In our infrastructure roadmap, we plan to aggressively expand our IT load dedicated to AI computing, backed by a broader global power infrastructure strategy. For developers, the operational takeaway is clear: reliable AI cloud capacity depends as much on power availability and thermal readiness as it does on the raw accelerator count.accelerator count.

What “Simplifying AI Infrastructure” Actually Means

Simplifying infrastructure does not mean hiding critical technical decisions. Rather, it means stripping away repetitive setup overhead while preserving the controls that truly matter in production: environment consistency, resource elasticity, workload observability, and the ability to transition from development to deployment without rebuilding the tech stack every single time.

Accelerated Environment Provisioning

AI teams should not have to manually stitch together drivers, frameworks, access rules, and runtime dependencies every time they test a workload. A mature GPU cloud workflow bypasses this friction via reproducible environments, making experiments highly portable and production readiness predictable.

Elastic GPU Cloud Capacity

AI compute demands are inherently non-linear. A prototype might require minimal compute, a training job might require a massive cluster for a short burst, and inference serving must scale dynamically with traffic. The true value of cloud GPU infrastructure lies in enabling teams to right-size resources around workload phases, rather than forcing projects to conform to fixed hardware constraints.

Workflow-Level Orchestration

As workloads scale, orchestration becomes the hallmark of infrastructure quality. Containerized tasks, model artifacts, data placement, observability, and deployment pipelines must flow smoothly under reproducible operational controls. Efficient orchestration minimizes friction between research, engineering, and MLOps, the exact gap where AI delivery typically slows down.

How We Transform Infrastructure Into an AI Cloud Platform

At Bitdeer AI, we view GPU cloud infrastructure as an integrated production system, not a fragmented catalog of isolated compute instances. Bitdeer AI Cloud delivers turnkey GPU cloud infrastructure for both training and inference, helping teams align compute choices with the technical DNA of their AI projects. Our goal is to eliminate setup friction, clarify deployment pathways, and bring compute design into lockstep with AI delivery.

GPU Cloud for Training

AI training is hyper-sensitive to VRAM capacity, data throughput, and distributed coordination. We help teams think across the entire training lifecycle: how data reaches the GPUs, how jobs scale across nodes, how environments maintain absolute consistency, and how engineers smoothly transition from experimentation to reproducible production pipelines.

Inference, Model Serving, and Model Studio

Inference introduces an entirely different set of infrastructure challenges, shifting the focus to responsiveness, concurrency, endpoint stability, and the ability to rapidly evaluate models before application integration. Model Studio supports this shift by simplifying model access and offering API-driven inference workflows, allowing teams to evaluate and integrate models with minimal operational overhead.

Security, Cost Predictability, and Operational Fit

For enterprise AI, technical performance alone is insufficient. Teams require workload isolation, reproducible access controls, cost visibility, clear resource planning, and cloud consumption models that align with how AI systems are actually built. These elements become critical as models transition from internal pilots to customer-facing or business-critical services.

Who Benefits the Most?

This infrastructure paradigm delivers maximum value to organizations ready to move beyond isolated experimentation:

Teams Transitioning from Research to Production

Engineering teams that need a clear, managed path from notebooks and early-stage experiments to reproducible training jobs and controlled deployment environments. Their core question is not just GPU access, but maintaining continuity across experimentation, validation, and release.

AI Product Teams Operating High-Scale Inference

Product teams focused on sustained service performance, cost visibility, and the flexibility to scale model capacity without re-architecting the entire system. An AI cloud platform becomes invaluable when it makes inference endpoints easier to evaluate, run, and scale.

Global Teams Building Agentic and Multimodal Workflows

AI agents, multimodal pipelines, and complex enterprise workflows place immense pressure on AI infrastructure, compute availability, and system orchestration. Because these workloads frequently combine inference, retrieval, tool calling, and model serving, the underlying GPU infrastructure, orchestration layer, and API design become core components of the application architecture itself.

The Takeaway: Production-Grade AI Demands Systems, Not Just GPUs

The central question is no longer whether AI teams need accelerators. They do. The more important question is whether the surrounding infrastructure makes those accelerators easier to use, easier to scale, and easier to connect to real deployment requirements.

What AI Teams Should Evaluate

When evaluating an AI cloud platform, teams should look beyond headline GPU availability. Factors like accelerator fit, memory bandwidth, interconnect strategy, storage throughput, orchestration models, inference workflows, security posture, operational visibility, and power-aware infrastructure planning will ultimately dictate how fast an AI system achieves production readiness.

Where Bitdeer AI Fits

Our focus at Bitdeer AI is to make this complex stack accessible through Bitdeer AI Cloud, Model Studio, and continued engagement with the NVIDIA ecosystem. We are not trying to reduce AI infrastructure to a buzzword.We are here to eliminate avoidable setup friction, empowering developers and enterprises to transition from experimentation to production with predictable performance, rigorous deployment discipline, and a clear path to scale.

Over the past year, artificial intelligence has rapidly transitioned from experimentation to real-world deployment. What was once considered an exploratory capability is now being embedded into core business processes across industries. Yet, despite the acceleration in model capabilities, a more fundamental challenge is emerging.

The question is no longer whether AI works.

It is whether organizations can make it work at scale.

In a recent roundtable hosted by Bitdeer AI, discussions with Singapore AI-native companies, business leaders , and technology experts revealed a consistent pattern: while AI capabilities are advancing quickly, deployment maturity is lagging behind. The gap between potential and execution is now the defining constraint.

Where AI Is Delivering Real Value

AI adoption today is increasingly driven by measurable improvements in quality and efficiency, rather than experimentation or curiosity. The strongest traction can be observed in domains where outputs are structured, repeatable, and directly tied to performance metrics.

In areas such as software development, customer support, and sales operations, AI has already demonstrated tangible productivity gains. In one example discussed, AI-enabled sales workflows significantly outperformed traditional approaches, with individual contributors achieving markedly higher output (“20X more efficient”) . While such figures should be interpreted cautiously, they illustrate a broader trend: AI is amplifying individual productivity in ways that were previously unattainable.

This pattern is even more pronounced in data-intensive environments. In auditing workflows, traditional human processes are inherently constrained by time and cognitive bandwidth, often capturing only a fraction of potential issues . By contrast, AI systems can process entire datasets and identify patterns across documents at scale. The result is not merely faster execution, but fundamentally deeper coverage.

However, adoption is not uniform. In creative domains such as image generation, human oversight remains critical, as off-the-shelf model outputs often fall short of production-grade expectations. This uneven distribution of value highlights an important principle: AI adoption does not happen everywhere at once—it concentrates first in areas where value is both immediate and measurable.

A New Model of Execution

AI is not only changing what companies build—it is changing how they build.

Traditional product development cycles, often measured in months, are being replaced by rapid iteration loops measured in weeks. Teams are prioritizing speed over perfection, launching “good enough” solutions early and refining them based on real-world feedback.

This shift is enabled by AI’s ability to lower the cost of experimentation. Building and testing new workflows is no longer prohibitively expensive, allowing organizations to iterate more aggressively and adapt more quickly. The implication is significant. Competitive advantage is no longer determined solely by planning or strategy, but by execution speed and adaptability.

Beyond operational efficiency, AI is increasingly being positioned as a driver of business value and market positioning.

One particularly notable trend discussed was the role of AI in pre-IPO transformation. Some companies are actively integrating AI capabilities prior to listing, using it to strengthen their narrative and enhance perceived growth potential (“AI transformation… higher valuation”) . In this context, AI becomes more than a tool—it becomes part of the company’s identity.

This signals a broader evolution in how AI is perceived:

It is no longer just an operational upgrade. It is a strategic asset.

The Organizational Constraint

Despite clear success in specific use cases, scaling AI across organizations remains challenging. At the organizational level, the barrier is rarely technical — it is structural. But even where alignment is achieved, a second and often more persistent challenge emerges as systems move into production.

AI initiatives are often driven from the top down, with leadership recognizing the strategic importance of adoption. However, this top-down momentum is frequently misaligned with incentives at other levels of the organization. Middle management may resist changes that reduce their operational control, while employees may perceive AI as a threat rather than an enabler.

This misalignment creates a familiar pattern: organizations successfully launch pilot projects but struggle to extend them into production. AI remains siloed, rather than becoming an integrated capability. Even in organizations where alignment exists, deployment introduces a different set of challenges.

From Models to Systems: Cost as the Dominant Constraint

Another key insight from the discussion is that AI deployment is not simply about selecting the right model. It is an ongoing engineering discipline that requires iteration, evaluation, and system design.

Teams described a progression that reflects increasing maturity: initial experimentation with prompting evolves into structured evaluation frameworks, followed by investment in context engineering, workflow orchestration, and ultimately model optimization. This process is rarely linear. Instead, it is characterized by continuous experimentation and refinement .

In this context, the role of engineering shifts. The challenge is no longer to make AI work in isolation, but to make it reliable, consistent, and scalable within production environments. Context management, memory design, and workflow integration become as important as the model itself. Among all these challenges, cost emerges as the most significant constraint.

Token consumption scales with usage, and for AI-native companies, costs can escalate rapidly. What begins as a manageable expense during experimentation can quickly become unsustainable at scale.

To address this, organizations are adopting increasingly sophisticated strategies. Rather than relying on a single model or provider, they are building hybrid architectures that balance performance and cost. Complex reasoning tasks are routed to high-performance models, while repetitive or well-defined workflows are handled by fine-tuned local models. In parallel, some teams are exploring edge deployment to reduce dependency on centralized infrastructure.

At the same time, optimization efforts extend beyond model selection. Teams are actively managing context length, compressing inputs, and building abstraction layers to avoid vendor lock-in. These approaches reflect a broader shift: AI deployment is increasingly a problem of cost-efficient orchestration.

What Enterprises Need Next

As AI systems become more complex, enterprises increasingly require infrastructure that is designed not just for experimentation, but for long-term operational scalability.

In practice, organizations are no longer managing a single model or workflow. Production AI environments often involve multiple models, varying workload types, hybrid deployment architectures, and continuously evolving cost-performance tradeoffs. This introduces a new operational challenge: orchestrating AI systems efficiently across compute, inference, storage, and deployment layers.

This is where AI-native cloud platforms are beginning to play a more important role.

Platforms such as Bitdeer AI are designed to help enterprises address these emerging deployment requirements through integrated GPU infrastructure, scalable inference environments, and flexible model deployment workflows. 

Rather than treating AI as a standalone application layer, the focus shifts toward enabling production-ready AI systems that can adapt to changing workload demands, cost constraints, and deployment strategies.

For example, with Bitdeer AI Cloud, businesses and AI teams can:

• Scale GPU resources flexibly for training, inference, embeddings, and automation workloads as demand changes.
• Support different deployment needs across cloud, dedicated infrastructure, and hybrid environments, with options such as bare metal, VM-based GPU services, and containerized workspaces.
• Improve infrastructure efficiency as token usage and inference demand grows, with better visibility into usage, costs, budgets, and resource allocation.
• Access and deploy open-source models more easily through flexible model workflows, API-based access, and integrated compute environments.
• Build AI agent workflows that connect business data, APIs, MCP tools, knowledge bases, and operational rules to support use cases such as customer support, document processing, reporting, knowledge search, and workflow automation.
• Operate AI workloads with stronger control, including managed databases, private network isolation, access controls, monitoring, and role-based management

In this way, Bitdeer AI Cloud helps businesses move beyond raw GPU capacity toward a more complete AI cloud environment — one that supports model development, deployment, and workflow automation from experimentation to production.

Conclusion

The evolution of AI has reached a point where access to models is no longer the differentiating factor. The competitive landscape is now defined by an organization’s ability to deploy, scale, and optimize AI effectively.

Success will depend on the ability to align organizational incentives, manage cost structures, and build systems that can evolve alongside rapidly advancing technology.

In this new phase, the question is not who has the best model.It is who can make AI work in the real world.

SINGAPORE, 26 June, 2026 — Bitdeer AI, part of Bitdeer Technologies Group (NASDAQ: BTDR) and a preferred NVIDIA Cloud Partner, today announced it has been the winner of “AI Cloud Platform of the Year” in 2026 AI Breakthrough Awards. The award is presented by AI Breakthrough, a leading market intelligence organization that recognizes the top companies, technologies and products in the global AI market today. The prestigious accolade recognizes Bitdeer AI’s architectural breakthrough in delivering a fully integrated, full-stack AI cloud environment optimized for enterprise-scale generative AI and production workloads.

As enterprises transition from localized AI experimentation to global production, traditional cloud architectures are fracturing under the weight of fragmented workflows, capacity shortages, and volatile pricing. Bitdeer AI solves these structural challenges through a fundamentally different approach: a vertically integrated "AI Factory" model. By owning and operating its high-performance data center infrastructure, Bitdeer AI eliminates third-party dependencies, optimizing performance directly from the physical facility and silicon level up through the core application software.

This ground-up ownership unlocks a highly unified, high-velocity software ecosystem. The Bitdeer AI cloud platform removes the friction of traditional deployment by bridging the entire AI lifecycle into a singular environment, spanning distributed training, multi-cluster scheduling, an expansive optimized model library, and serverless inference APIs.

By operating a completely integrated stack, Bitdeer AI delivers distinct enterprise advantages that redefine the AI cloud category:

• Seamless Workflow Velocity: Eliminates the need to stitch together fragmented services, allowing organizations to move from raw data to fine-tuning and sustained inference within a single, cohesive platform.
• Uncompromised Flexibility & Control: Grants developers granular control over their compute environments, offering a seamless choice between Bare Metal instances for maximum raw performance or Virtual Machines (VMs) for rapid, elastic scaling.
• Structural Cost Stability: By eliminating the margins associated with third-party infrastructure hosting, Bitdeer AI passes unprecedented price-performance leadership and transparent cost structures directly to users.
• Deep Silicon Optimization: As a preferred NVIDIA Cloud Partner, Bitdeer AI deeply integrates NVIDIA’s hardware and enterprise software stack. Controlling the physical data center allows Bitdeer AI to optimize power density and cooling specifically for next-generation NVIDIA architectures, granting clients guaranteed capacity and day-0 access to the latest software and model capabilities.

"Bitdeer AI has built one of the most complete and capable full-stack AI cloud platforms available today, combining high-performance GPU infrastructure with an enterprise-ready AI development environment," said Steve Johansson, Managing Director, AI Breakthrough. 

"We are honored to receive this recognition from AI Breakthrough for the second consecutive year," said Retainna Lin, VP of Bitdeer AI Cloud. "The winners of the next era of AI will be determined by execution speed and infrastructure reliability. At Bitdeer AI, we have engineered a full-stack cloud platform that compresses the journey from raw GPU compute to live production. We are building the foundational operating layer for global AI deployment."

Looking ahead, as the AI landscape shifts from experimentation into a mature phase defined by global production, Bitdeer AI is positioned to serve as the industry's foundational architecture. Moving forward, the company is executing on a multi-phase strategy to scale its unified cloud ecosystem globally—expanding its footprint of sustainable, proprietary "AI Factories" to meet regional growth and strict data residency requirements, deepening its integration with NVIDIA to support next-generation architectures alongside day-0 model and software releases, and continuously evolving its platform features into the definitive, full-stack operating layer for enterprise AI at scale.

Forward-Looking Statements

This press release may contain forward-looking statements regarding Bitdeer AI’s anticipated future performance, market opportunities, and business strategies. These statements are based on current beliefs, assumptions, and expectations, and are subject to risks and uncertainties that could cause actual results to differ materially. Bitdeer AI disclaims any obligation to update or revise these forward-looking statements to reflect future events or developments, except as required by law.

As autonomous agents take on increasingly complex, long-running tasks, from orchestrating multi-week coding projects to synthesizing hundreds of research sources in real time, the demands placed on the underlying reasoning model have fundamentally changed. Speed, depth of reasoning, and the ability to sustain context across extended sessions are no longer optional. They are the baseline for any model serious about agentic work.

Today, we are delighted to announce that NVIDIA Nemotron™ 3 Ultra is available at launch on Bitdeer AI Model Studio. As the flagship of the NVIDIA Nemotron family, Nemotron 3 Ultra is an open frontier reasoning model purpose-built or long-running autonomous agents. Designed to be smaller, faster, and lower cost for agent workflows, it delivers up to 5x faster inference and up to 30% lower cost while maintaining frontier-level reasoning capabilities for coding, deep research, and enterprise automation.

What is NVIDIA Nemotron 3 Ultra?

NVIDIA Nemotron 3 Ultra is an open frontier-reasoning model built for long-running autonomous agents. It is optimized for agent orchestration, complex reasoning, coding, and deep research workloads where speed, cost efficiency, and sustained reasoning depth matter as much as raw intelligence. Unlike traditional chat-focused models Nemotron 3 Ultra is designed for workflows that span hundreds of turns, multiple tools, and extended executive cycles—delivering up to 5x faster inference and up to 30% lower cost for agentic workloads

Post-trained for agent harnesses, Nemotron 3 Ultra sustains reasoning depth across the hardest calls — architectural decisions across week-long autonomous coding sessions, synthesis across hundreds of contradictory research sources, or verification of chip designs across thousands of interdependent constraints.

Fully open, Nemotron 3 Ultra can be fine-tuned for any domain and deployed on any infrastructure, giving enterprises the flexibility to maintain data control while benefiting from frontier-class intelligence.

Key Specifications

Why Faster and Lower-Cost Agent Reasoning Matters?

Most production agentic systems are not bottlenecked by a single model call — they are bottlenecked by the cumulative cost of many. A coding agent planning a complex refactor, a research agent cross-referencing hundreds of sources, or an enterprise agent triaging thousands of alerts all depend on how quickly the model can complete each reasoning cycle. Throughput is not just a hardware metric; it is a direct multiplier on how much an agent can accomplish within any given time budget.

Nemotron 3 Ultra addresses this at the architectural level:

Fastest Task Completion. Ultra's Hybrid Mamba-Transformer MoE architecture delivers the highest token throughput in NVIDIA’s published comparisons against leading open frontier baselines, enabling more reasoning cycles per time budget. Multi-Token Prediction (MTP) further reduces generation time for long sequences by predicting multiple future tokens in a single forward pass, and NVFP4 precision, optimized specifically for NVIDIA Blackwell GPUs, delivers significant inference speedup versus FP8 while maintaining accuracy.

Leading Accuracy. Latent MoE enables Ultra to call four experts for the inference cost of just one, improving intelligence and generalization with no added compute cost. Multi-environment reinforcement learning training across a broad set of agentic environments gives the model robust tool calling, reasoning, and instruction-following capabilities. The 1M token context window retains conversation history and plan states across long-running agent sessions, and enables cross-document reasoning at a scale that shorter-context models cannot match.

Fully Open. Ultra is released with open weights under NVIDIA's open-model license, trained on NVIDIA-generated high-quality synthetic data that is fully open, and accompanied by published development techniques and recipes, giving researchers and enterprises full transparency and the flexibility to customize or build on top of the model.

Enterprise Use Cases

Source: NVIDIA

Programming and Coding Agents. Coding agents built on Ultra plan, code, test, debug, and iterate end-to-end across large codebases. Ultra handles the hard reasoning calls: architectural planning, complex multi-file refactors, and error recovery, sustaining coherent reasoning across sessions that can span days or weeks.

Deep Research and Search. Research agents search, evaluate, cross-reference, and synthesize across hundreds of sources in sustained parallel loops. Ultra handles final synthesis, the step where contradictions must be resolved, gaps must be identified, and novel hypotheses proposed with the depth and consistency that frontier reasoning demands.

Enterprise Workflow Agents. Agents built for enterprise workflows automate operations across industries in persistent, tool-using loops: triaging thousands of security alerts, ingesting and interpreting regulatory filings, orchestrating clinical trial operations. Ultra handles the complex reasoning steps within these workflows, where errors in judgment have real downstream consequences.

EDA and Chip Design. Chip design agents autonomously generate RTL from specifications, verify designs across thousands of constraints, and orchestrate workflows from design to manufacturing sign-off. Ultra handles verification, failure analysis, and cross-block dependency resolution — the reasoning-intensive operations that define the quality of the final design.

Supported Agentic Frameworks

Nemotron 3 Ultra integrates with leading open agent frameworks out of the box — from single-command deployment with NVIDIA NemoClaw to tested cookbooks for the most popular coding and orchestration platforms. It is also packaged as an NVIDIA NIM microservice, making it deployable across data center and cloud environments without modification.

Run Nemotron 3 Ultra Via API on Bitdeer AI Model Studio

You can run Nemotron 3 Ultra on Bitdeer AI Model Studio, our serverless inference platform designed to make access to advanced foundation models simple and scalable.With a unified API, Model Studio allows developers and enterprises to start using models quickly without managing underlying infrastructure, reducing deployment complexity and time to value.

Bitdeer AI is a preferred NVIDIA Cloud Partner, certified to ISO/IEC 27001:2022 and SOC2 Type I & Type II, providing the secure, compliant, high-performance, and enterprise-grade infrastructure that production agentic AI deployments require. Run your models at the precision and scale your business requires, on Bitdeer AI's purpose-built GPU fleet.

Get Started

1. Log in to Bitdeer AI Model Studio
2. Locate NVIDIA Nemotron 3 Ultra in the model list
3. Generate an API key and start making API calls

This streamlined workflow enables rapid integration of frontier reasoning capabilities into applications and agent systems.

Conclusion

NVIDIA Nemotron 3 Ultra raises the bar for what open frontier models can deliver in production. Combining frontier-class intelligence with up to 5x faster inference and up to 30% lower cost, Ultra is designed specifically for the demands of long-running autonomous agents. With a 1M - token context window, open weights, open training recipes, and  With day-0 availability on Bitdeer AI Model Studio, organizations can move quickly from experimentation to production while maintaining performance, flexibility, and economics required for large-scale agentic AI.

As AI agents evolve beyond text and into real-world workflows, the ability to understand information and reason across multiple modalities is becoming essential. From video and audio to documents and UI screens, modern agentic systems require models that can reason across modalities with accuracy, efficiency, and enterprise readiness. 

Today, we’re delighted to announce that NVIDIA Nemotron™ 3 Nano Omni is available at launch on our Bitdeer AI Model Studio. As part of the broader NVIDIA Nemotron family, it represents a new step forward in open, production-ready multimodal reasoning models.

What is NVIDIA Nemotron 3 Nano Omni

NVIDIA Nemotron 3 Nano Omni is an open multimodal foundation model designed for production agentic AI. It unifies reasoning across video, audio, images, documents, charts, and text in a single model—eliminating the need for fragmented multimodal pipelines.

By consolidating perception and reasoning into one system, Nemotron 3 Nano Omni simplifies agent development, reduces orchestration complexity, improves efficiency and scalability, and delivers leading multimodal accuracy. Built with open weights, datasets, and recipes, it enables developers and enterprises to customize, deploy, and operate multimodal agents with full control and flexibility.

Key Specifications:

●  Model Size: 30B A3B

●  Modalities: Input (text, image, video, audio), Output (text).

●  Architecture: Hybrid Mixture of Experts (MoE) with Transformer-Mamba design

●  VIsion Encoder: CRADIO v4-H

●  Audio ENcoder: Parakeet

●  Context Length: 256k

●      Optimizations:

• Conv3D fortemporal video reasoning
• Efficient Video Sampling (EVS) for lower inference cost

●      Quantization: FP8, NVFP4

Why a Unified Multimodal Model Matters

Many enterprise AI systems still rely on stitched-together pipelines across vision, speech, OCR, and reasoning models. This approach introduces higher latency from repeated inference passes, increased operational complexity, and fragmented context across modalities.

Nemotron 3 Nano Omni addresses this by acting as a multimodal perception and reasoning layer within agent systems—enabling a unified perception → reasoning → action loop.

Enterprise use cases

Customer Service Agent: A customer service agent operates in a highly multimodal environment. It analyzes recorded customer interactions, including audio and speech transcriptions. It reasons over screen recordings of customer sessions and images such as screenshots of errors or invoices. At the same time, it reads documents like knowledge‑base articles, policies, and CRM history. Nemotron 3 Nano Omni unifies all of these signals so the agent understands not just what the customer said, but what they experienced and what the business rules allow—enabling accurate, context‑aware resolution.

Financial Analyst Agent: Financial analysis depends on more than text alone. This agent reasons across documents like financial filings and earnings transcripts, images such as charts and scanned reports, audio and speech from earnings calls, and video from investor presentations. Nemotron 3 Nano Omni ties together what executives say, how numbers are presented visually, and what the underlying documents show—producing grounded insights rather than surface‑level summaries.

Computer Use Agent: The computer use agent is one of the clearest demonstrations of unified multimodality. It analyzes video and images from screen recordings to understand UI state over time, interprets instructions and system audio cues, and reads documents like task instructions and validation policies. Nemotron 3 Nano Omni enables the agent to see the interface, understand intent, read constraints, and take the correct action—all within one reasoning loop. This collapses when perception and decision‑making are split across models.

Media and Entertainment Agent: Media workflows depend on more than transcripts alone. This agent reasons across video content, dialogue, on-screen text, and visual scene changes to support richer video and speech analysis. Nemotron 3 Nano Omni can generate dense captions that capture not just what is said, but what appears and happens on screen, while also improving video search and summarization across large content libraries. This helps media teams turn raw footage into searchable, contextualized, and production-ready assets more efficiently.

Run Nemotron 3 Nano Omni Via API on Bitdeer AI Model Studio

You can run Nemotron 3 Nano Omni on Bitdeer AI Model Studio, our serverless inference platform designed to make access to advanced foundation models simple and scalable. With a straightforward API, Model Studio allows developers and enterprises to start using models quickly without managing underlying infrastructure, reducing deployment complexity and time to value.

This makes it easier to integrate multimodal reasoning capabilities into applications and agentic workflows, while benefiting from a more flexible and efficient path from experimentation to production in just a few steps.

Get Started

1. Log in to Bitdeer AI Model Studio
2. Locate Nemotron 3 Nano Omni in the model list
3. Generate API key and start making API calls

This streamlined workflow enables rapid integration of multimodal reasoning into applications and agent systems.

Conclusion

NVIDIA Nemotron 3 Nano Omni makes multimodal AI more practical for real-world deployments. Instead of managing multiple models and pipelines, teams can focus on building agentic applications, automating workflows, and delivering better user experiences.

With availability on Bitdeer AI Model Studio, organizations can move from experiment to production faster—turning multimodal AI into measurable business impact.

OpenClaw has surpassed 290,000 stars on GitHub, making it one of the most popular open-source AI Agent frameworks of 2026. A growing number of developers and tech enthusiasts are experimenting with deploying their own AI assistants locally.

However, local deployment presents significant challenges in practice: service interruptions when the machine shuts down, complex environment configuration, API key security risks, and more — all of which severely limit the continuous availability of an AI Agent.

Deploying OpenClaw to the cloud addresses these issues at their root. This article examines the key advantages of cloud deployment, provides guidance on selecting the right large language model, and demonstrates real-world use cases for OpenClaw.

What Is OpenClaw?

OpenClaw is an open-source personal AI Agent framework built with TypeScript. Its core capabilities include:

• 24/7 Autonomous Operation: A built-in heartbeat mechanism proactively monitors tasks and takes action without requiring continuous user intervention
• 50+ Platform Integrations: Supports WhatsApp, Telegram, Discord, Slack, Gmail, GitHub, and other major platforms for unified cross-platform management
• Model Flexibility: Compatible with cloud-based LLMs such as Claude, GPT, and Grok, as well as local models via Ollama / vLLM
• Extensible Skills System: Skills are defined using Markdown + YAML, with 100+ pre-built skills available on ClawHub
• Privacy-First Design: All data is stored locally by default, with the memory system based on local Markdown files

Unlike traditional chatbots, OpenClaw features contextual memory, proactive behavior, and tool invocation capabilities — functioning more as a persistent AI collaborator than a simple Q&A interface.

Cloud Deployment vs. Local Deployment

While OpenClaw supports local installation, cloud deployment offers clear advantages for users who require stability and continuous availability.

Local deployment is suitable for short-term testing, while cloud deployment is what enables an AI Agent to deliver continuous, reliable service. An assistant that needs to be always available requires an environment that is always online.

Deployment Cost

Running OpenClaw does not require GPU resources. A basic CPU virtual machine instance (2 cores / 4GB RAM / 20GB SSD) is sufficient for stable operation. At this configuration, Bitdeer AI Cloud's on-demand pricing is approximately $0.0363/hour (around $26/month) — providing a 24/7 online AI assistant environment at a fraction of typical infrastructure costs.

Bitdeer AI Cloud, as an NVIDIA Preferred Cloud Service Provider, offers compute resources spanning multiple generations of NVIDIA GPUs including H100, H200, B200, and GB200. For Agent deployment scenarios like OpenClaw, a CPU-only instance is all that's needed. For advanced use cases such as model fine-tuning or local inference, GPU instances are available for seamless scaling. Virtual machine instances are billed on-demand — for detailed pricing, refer to the GPU Compute Pricing page.

Model inference is handled via API calls and billed per token. Bitdeer AI Cloud provides multi-vendor inference services covering text generation and other task types. For detailed inference pricing, refer to the AI Models Pricing page.

Model Selection Guide

One of OpenClaw's core strengths is model flexibility — it is not locked into any single LLM provider. Bitdeer AI Cloud currently offers 40+ models from 10+ providers, spanning text generation, visual understanding, reasoning, and image generation. All APIs are OpenAI-compatible, making model switching nearly effortless.

Below are representative text generation models suitable for OpenClaw, with pricing reference:

Scenario-Based Recommendations

• Best overall value — DeepSeek-V3.2, with the lowest combined input/output cost among flagship models and industry-leading code and reasoning capabilities
• Long-context input scenarios (e.g., document analysis, email summarization) — Qwen3-235B-A22B, with input pricing as low as $0.22 per million tokens
• Deep conversational AI — Kimi-K2.5, with excellent language comprehension for high-quality conversational output
• Ultra-low cost — The platform also offers lightweight models such as Gemma and Ministral, with input pricing starting at $0.02 per million tokens for high-frequency, low-complexity tasks

For the full model catalog and interactive demos, visit Bitdeer Model Explore.

OpenClaw integrates with Bitdeer's model service entirely through its configuration file — users simply enter their API key and model name in openclaw.json, and OpenClaw handles all API communication automatically with no additional code required. All Bitdeer model APIs are OpenAI REST-compatible, allowing OpenClaw to recognize and invoke them directly.

API keys can be generated in the Bitdeer AI Cloud console under API Keys.

Deployment Guide

Bitdeer AI has published a comprehensive deployment tutorial covering everything from creating a cloud instance and installing OpenClaw to configuring model connections and integrating with Telegram. For the full step-by-step guide, refer to:

Installing and Configuring OpenClaw on Bitdeer AI Cloud

The deployment process can be summarized in five steps:

1. Create a Cloud Instance — Provision a virtual machine on Bitdeer AI Cloud
2. Install OpenClaw — Run the official one-line installation script
3. Configure Models — Enter your Bitdeer AI API key and specify the LLM
4. Connect Telegram — Pair with a Telegram Bot for mobile access
5. Run in Background — The service runs continuously 24/7 with no manual maintenance required

Real-World Use Cases

Once deployed, OpenClaw delivers practical value across multiple dimensions. Below are several representative use cases:

Personal Productivity

• Connect to Gmail to automatically read emails, extract action items, and send daily digests on a set schedule
• Integrate with Google Calendar to create and manage events directly through conversation
• Perform real-time multilingual translation across English, Chinese, Japanese, Korean, and more

Research and Analysis

• Execute web searches, read page content, and generate structured summaries
• Extract key points and summaries from any given URL
• Conduct multi-step research with comparative analysis and structured report output

Code Generation and Execution

• Generate and execute code in a sandboxed environment, returning results directly
• Support for Python, JavaScript, and other mainstream languages for data processing and scripting
• Combine web search capabilities with code generation to automatically reference documentation and produce runnable solutions

These capabilities are powered by OpenClaw's skills system. Over 100 pre-built skills are available on ClawHub for immediate use. For custom skill development, refer to the OpenClaw Skills Documentation.

Conclusion

As a fully featured open-source AI Agent framework, OpenClaw — combined with Bitdeer AI Cloud's infrastructure and model inference services — provides a stable, secure, and always-online AI assistant solution.

Get Staarted

• Sign up for Bitdeer AI Cloud
• View the full deployment tutorial
• Visit the OpenClaw GitHub repository
• Bitdeer AI Documentation Center

Note:Pricing for models and GPU resources is subject to change. Please refer to the platform for the most up-to-date pricing.

Deploy secure, production-ready AI agents in minutes with NVIDIA NemoClaw on Bitdeer AI Cloud. In this guide, you’ll learn how to quickly set up a fully sandboxed environment and run powerful models like Moonshot AI’s Kimi K2.5 through Bitdeer AI’s high-performance inference endpoints.

What You'll Build

By the end of this guide, you'll have:

• A secure, sandboxed AI agent running on Bitdeer AI Cloud
• Access to powerful models like Kimi K2.5 through Bitdeer AI's inference API
• Full network policy controls and monitoring capabilities

Why NemoClaw on Bitdeer AI?

NemoClaw on Bitdeer AI Cloud brings together secure agent execution with scalable infrastructure and high-performance inference. While sandboxed execution with Landlock, seccomp, and network isolation is handled within the NemoClaw and OpenShell framework, Bitdeer AI Cloud can provide the underlying compute environment, enabling on-demand scaling of CPU and GPU resources with usage-based pricing. Our platform also delivers access to optimized inference endpoints for running leading models, along with network-level controls to help manage how agents interact with external services, ensuring both flexibility and operational control.

Prerequisites

NemoClaw System Requirements Minimum: 4+ vCPU, 16 GB RAM, 40 GB of free disk space, and Ubuntu 22.04 LTS.

One Bitdeer AI Cloud Requirements

• Active Bitdeer AI Cloud account
• API key from Bitdeer AI Cloud (for inference)

Bitdeer AI Instance: Select g4a.xlarge (4 vCPU, 16 GB RAM)

Important: The sandbox image is approximately 2.4 GB compressed. For instances with 16 GB RAM, you'll have sufficient memory for smooth operation.

Software Dependencies

• Node.js 20 or later
• npm 10 or later
• Docker (installed and running)
• OpenShell CLI (installed by NemoClaw)

1.1 Create Virtual Machine

1. Log in to your Bitdeer AI Cloud account

2. Navigate to Virtual Machine services

3. Click Create Instance and select:

Instance Type: g4a.xlarge (4 vCPU, 16 GB RAM)

Storage: 40 GB SSD minimum

OS: Ubuntu 22.04 LTS

Region: Choose nearest to your location

4. Complete the order and wait for instance provisioning

5. Once running, note the public IP address

1.2 Access Your Instance

Connect via SSH:

ssh ubuntu@<your-instance-ip>

Step 2: Install Docker

NemoClaw requires Docker as the container runtime:

# Update package index
sudo apt-get update

# Install Docker (service starts automatically)
sudo apt-get install -y docker.io

# Add user to docker group
sudo usermod -aG docker $USER

Important: Log out and log back in for group changes to take effect:

exit
ssh ubuntu@<your-instance-ip>

Verify Docker installation:

docker --version
docker run hello-world

Step 3: Get Bitdeer AI API Key

Before installing NemoClaw, obtain your Bitdeer AI API key:

1. Log in to Bitdeer AI Cloud

2. Navigate to Models → API Keys

3. Click Generate API Key

4. Copy the generated key and save it securely

⚠ Security Warning: Never commit API keys to version control or share them publicly.

Step 4: Install and Configure NemoClaw

Download and run the NemoClaw installer, which includes an interactive wizard for configuring your Bitdeer AI inference endpoint:

curl -fsSL https://www.nvidia.com/nemoclaw.sh | bash

During the Installation Wizard

When prompted, configure Bitdeer AI as your inference provider:

[2/7] Configuring inference (NIM)
──────────────────────────────────────────────────

Inference options:
  1) NVIDIA Endpoints (recommended)
  2) OpenAI
  3) Other OpenAI-compatible endpoint  ← Select this
  4) Anthropic
  5) Other Anthropic-compatible endpoint
  6) Google Gemini

Choose [1]: 3

OpenAI-compatible base URL: https://api-inference.bitdeer.ai/v1
Other OpenAI-compatible endpoint API key: <paste-your-bitdeer-api-key>

Other OpenAI-compatible endpoint model []: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B
# or: moonshotai/Kimi-K2.5

The wizard will:

• Install Node.js 20+ if not present
• Install npm packages
• Set up the NemoClaw CLI
• Configure Bitdeer AI as your inference provider
• Save your API key securely to ~/.nemoclaw/credentials.json
• Create your first sandbox

Post-Installation Summary

After installation completes, you should see:

──────────────────────────────────────────────────
Sandbox      my-bitdeer-agent (Landlock + seccomp + netns)
Model        nvidia/NVIDIA-Nemotron-3-Super-120B-A12B (Bitdeer AI Endpoint)
──────────────────────────────────────────────────
Run:         nemoclaw my-bitdeer-agent connect
Status:      nemoclaw my-bitdeer-agent status
Logs:        nemoclaw my-bitdeer-agent logs --follow
──────────────────────────────────────────────────

[INFO]  === Installation complete ===

If nemoclaw is not found after installation, run:

source ~/.bashrc
# or for zsh users:
source ~/.zshrc

Verify installation:

nemoclaw --version

Step 5: Connect and Chat with Your Agent

5.1 Connect to the Sandbox

nemoclaw my-bitdeer-agent connect

You'll enter the sandbox shell:

sandbox@my-bitdeer-agent:~$

5.2 Interactive TUI Mode

Launch the OpenClaw TUI for interactive chat:

openclaw tui

Send test messages and verify responses.

5.3 CLI Mode

For programmatic access or long outputs, use the CLI:

openclaw agent --agent main --local -m "Hello, how can you help me today?" --session-id test

To exit the sandbox shell:

exit

What's Next?

Now that you have your NemoClaw agent running on Bitdeer AI Cloud, here are some ways to extend it:

• Connect to Telegram

Enable mobile access to your agent:

nemoclaw my-bitdeer-agent telegram-setup

• Try Different Models

Experiment with other available models through Bitdeer AI Model Studio:

openshell inference set --provider openai --model zai-org/GLM-5

• Monitor Activity

Launch the OpenShell TUI to watch your agent in action:

openshell tui

• Customize Security

Define custom network policies to control what your agent can access.

Conclusion

With NemoClaw running on Bitdeer AI Cloud, deploying secure, production-ready AI agents becomes both practical and scalable. By combining NemoClaw’s sandboxed execution and policy framework with Bitdeer AI’s flexible infrastructure and optimized inference endpoints, you can move from setup to real-world usage in just a few steps. Whether you are experimenting with AI agents or building toward production, Bitdeer AI Cloud offers a reliable environment to run, scale, and manage your workloads with confidence.

Resources

Documentation:

• NemoClaw Docs
• OpenShell Docs
• Bitdeer AI Cloud

Community & Support:

• NemoClaw GitHub
• NemoClaw Discord
• Bitdeer AI Support

Recently, the emergence of OpenClaw drew attention to a growing demand for a new class of AI systems. Often referred to as “claws,” these systems represent long-running, autonomous, self-evolving agents that can plan and execute multi-step workflows with limited user intervention. Unlike traditional AI assistants, claws are not limited to single-turn interactions. They can access local files, interact with applications and external tools, and dynamically orchestrate sub-agents to break down complex tasks. More importantly, they are able to continuously refine strategies, decide where tasks should be executed, and optimize outcomes over time.

At the same time, this evolution introduces new challenges. As agents are granted broader access to data, tools, and infrastructure, concerns around security, privacy, and governance become significantly more critical. Questions around access control, data flow, model usage, and execution boundaries are no longer theoretical, but essential for deploying these systems safely in production.

To address the growing need for safer and more controllable agent execution, at NVIDIA GTC 2026, NVIDIA introduced NVIDIA NemoClaw for OpenClaw, an open source stack that adds privacy and security controls to long-running, autonomous agents. Built on NVIDIA Agent Toolkit, NemoClaw serves as an integration layer that enables OpenClaw agents to run within the newly introduced NVIDIA OpenShell runtime, combining open models such as NVIDIA Nemotron with controlled execution environments to make always-on agents safer and more practical to deploy.

NVIDIA OpenShell: Powering Safer Agent Execution for NemoClaw

NVIDIA OpenShell is an open-source runtime designed to make autonomous AI agents safer and more practical to deploy.  It addresses this by acting as a control layer between the agent and the underlying infrastructure, governing how the agent executes, what it can access, and where inference is routed.

Instead of relying solely on prompts or built-in guardrails inside the agent, OpenShell enforces policies at the runtime level. It helps bridge the gap between experimentation and production by allowing developers to deploy autonomous, self-evolving agents with clearer boundaries around privacy, security, and infrastructure access.

How OpenShell Is Built for Safer Agents

OpenShell uses a gateway to manage one or more isolated sandboxes where agents run. To understand how it works, it helps to look at it from two complementary angles: core components and protection layers. The components describe how the system is structured and managed, while the protection layers describe where policy enforcement is applied.

• The gateway acts as the control plane, coordinating sandbox lifecycle, authentication, providers, and policy management.
• The sandbox is the isolated runtime where the agent runs. The policy engine enforces constraints across filesystem, network, and process behavior.
• The privacy router governs inference routing, helping keep sensitive context under tighter control and directing model traffic according to defined privacy and cost policies.

These components enforce control across four protection layers: filesystem, network, process, and inference. Filesystem and process controls are locked when the sandbox is created, while network and inference controls can be updated at runtime. Together, this gives OpenShell a defense-in-depth model that supports long-running agents while maintaining clearer privacy, security, and governance boundaries.

To better understand how OpenShell enables safer autonomous agents, it helps to look at how its architecture is structured. The diagram below illustrates the key components that work together to govern execution, enforce policy, and control inference. 

OpenShell’s architecture for safer autonomous agents, illustrating the core components: the sandbox, the policy engine, and the privacy router

Deploy AI Agent Workloads with Bitdeer AI Cloud

Bitdeer AI Cloud is among the first NVIDIA Cloud Partners (NCPs) in Asia Pacific available as a model provider in the region, helping developers support the model deployment and inference behind OpenShell-based agent workloads

Bitdeer AI Cloud provides access to a wide range of high-performance NVIDIA AI infrastructure, including NVIDIA GB200 NVL72, NVIDIA HGX H200. Workloads can be deployed across multiple GPU configurations, with built-in support for load balancing and dynamic scaling to optimize performance, cost, and availability.

This enables developers to power the model deployment and inference required by agent workloads with the performance and reliability needed for production.

How to Get Started

Running models with Bitdeer AI Cloud is straightforward.  Start building on build.nvidia.com, deploy to Bitdeer AI Cloud:

First, select a model from the NVIDIA model catalog, such as Kimi K2.5 or Nemotron 3 Super, and navigate to the Deploy section.

Then, under Partner Endpoints, choose Bitdeer AI as your model provider. This allows the underlying model deployment and inference for agent workloads to be powered by Bitdeer AI Cloud, backed by scalable, production-grade NVIDIA AI infrastructure.

Once selected, you can proceed with deployment using serverless endpoints or dedicated GPU-backed infrastructure, depending on your workload requirements.

From there, OpenShell-based agents can leverage this setup to execute tasks, call tools, and operate within controlled environments, while running on scalable, production-grade compute.

By combining NVIDIA OpenShell’s secure runtime with Bitdeer AI’s infrastructure, developers can:

• Run long-running agents on dedicated, high-performance GPU clusters
• Scale workloads without managing underlying infrastructure
• Maintain stronger control over privacy, security, and inference routing

This integration bridges the gap between agent development and real-world deployment, making it easier to move from experimentation to scalable production systems.

Closing Thoughts

What stood out at NVIDIA GTC 2026 is that AI is shifting from prompt-based interactions to long-running, autonomous systems. As token consumption continues to grow, it is increasingly driven by agents themselves, reasoning over longer contexts, coordinating tools, and executing tasks over time.

This evolution brings new requirements beyond model performance. It becomes equally important how agents are executed, how data is governed, and how systems remain controlled in production environments. NVIDIA OpenShell reflects this shift by introducing a runtime layer designed for safer and more structured agent execution.

While OpenShell introduces a safer runtime layer for agent execution, Bitdeer AI Cloud serves as a model provider, delivering the scalable compute foundation needed to support real-world deployment of these systems. By providing scalable, high-performance AI compute, we aim to support developers in building and operating long-running agents within environments that prioritize privacy, security, and reliability.

Resource:

1. NVIDIA Developer Guide:  https://docs.nvidia.com/openshell/latest/about/overview.html
2. NVIDIA blog: https://developer.nvidia.com/blog/run-autonomous-self-evolving-agents-more-safely-with-nvidia-openshell/

• The Inference Inflection: GTC keynote explained massive shift in compute demand as AI moves from pre-training to real-time agentic inference.
• The NVIDIA Vera Rubin Platform: A "generational leap" featuring seven new chips (including NVIDIA Vera CPU and NVIDIA Rubin GPU) and five specialized racks designed to power the world’s largest AI factories.
• Disaggregated Inference: The newly integrated NVIDIA Groq 3 LPX rack unites with the Vera Rubin platform to deliver up to 35x higher inference throughput per megawatt.

The "Moment of Claws": NVIDIA is driving the next AI inflection point through NVIDIA OpenShell, an open-source runtime that enables autonomous agents to reason and act safely within isolated sandboxes.

The $1 Trillion Inference Inflection

At GTC 2026, the keynote underscored the massive shift from pre-training models to real-time execution, Huang revealed a staggering jump in the demand for AI infrastructure

The doubling of demand is driven by the era of agentic AI—systems that plan tasks, run tools, and validate results. Computing is no longer retrieval-based; it is growing from generative, to reasoning-oriented and finally agentic. Now, as AI Agents begin to perform different tasks through iterative processes of planning, breaking down tasks, reasoning and reflecting, the burst of inference compute demand has grown by roughly 1,000,000x in just two years.

Vera Rubin: The Foundation of the AI Factory

NVIDIA launched the Vera Rubin platform, a supercomputer where multiple racks work together as one massive, coherent system to maximize tokens per watt.

• NVIDIA Groq 3 LPX Rack: Marks a milestone in accelerated computing by enabling disaggregated inference. While the Rubin GPU handles the prefill, decode attention, and generating massive KV cache, the NVIDIA Groq LPU manages the low-latency decode phase, providing up to 10x more revenue opportunity for trillion-parameter models.
• Vera CPU Rack: The world’s first processor purpose-built for agentic AI. It delivers results twice as efficiently and 50% faster than traditional CPUs. With 88 custom Olympus cores and 1.5TB of LPDDR5X memory, it provides the single-threaded performance required for agents to run code and validate results.
• NVIDIA  BlueField-4 STX Rack: An AI-native storage infrastructure that treats data as "Context Memory." Using the NVIDIA DOCA Memos™ framework, it boosts inference throughput by up to 5x for long-context reasoning

NVIDIA OpenShell: Enabling the "Moment of Claws"

In early 2026, the emergence of OpenClaw drew attention to the demand for autonomous agents capable of executing multi-step workflows. By enabling agents to interact with local files and external tools, frameworks like OpenClaw illustrated a shift toward persistent, execution-oriented systems.

To support the safe deployment of these SOTA agents, NVIDIA is celebrating the “moment of claws” with NVIDIA OpenShell, an open-source runtime designed to govern how agents execute.

We’re also working with NVIDIA on NVIDIA NemoClaw — an open source stack that simplifies running OpenClaw always-on assistants, more safely, with a single command. As part of the NVIDIA Agent Toolkit, it installs the NVIDIA OpenShell runtime—a secure environment for running autonomous agents, and open source models like NVIDIA Nemotron.

NVIDIA OpenShell sits between the agent and the infrastructure, running any coding agent—OpenClaw, Claude Code, Cursor, or Codex—in an isolated sandbox with zero code changes. Every action is policy-enforced at the infrastructure layer, ensuring privacy and security for long-running agentic workflows.

Source: NVIDIA GTC 2026 Keynote

In tandem with these global advancements, Bitdeer AI is leading the charge for secure agentic infrastructure in the Asia Pacific region. Developers and enterprises can now find Bitdeer AI featured on build.nvidia.com as a provider for the infrastructure necessary to run the underlying "brains" of these agents.

We are also proud to highlight that Nemotron 3 Super is available on our platform. As the best-performing open model for OpenClaw, Nemotron 3 Super provides the high-level reasoning and planning capabilities required to fuel complex autonomous workflows.

Start building your own safe autonomous agent today directly from build.nvidia.com:

1. Access NVIDIA OpenShell on build.nvidia.com.
2. Seamlessly integrate frontier open model endpoints from Bitdeer AI to power your long-running agents.

Powered by NVIDIA GB200 NVL72 systems deployed in our state-of-the-art AI data centers, Bitdeer AI Cloud delivers the high-performance infrastructure required to run autonomous, self-evolving agents in NVIDIA OpenShell at scale. By combining secure runtime governance with high-performance AI cloud, we empower developers to build the next generation of AI with stronger privacy and infrastructure reliability

Source: https://blogs.nvidia.com/blog/gtc-2026-news/

Bitdeer AI Model Studio now supports NVIDIA-Nemotron-3-Super-120B-A12B, bringing a new open model for advanced reasoning, long-context understanding, and agentic AI development to the platform.

Designed for complex multi-agent applications, Nemotron 3 Super combines strong reasoning accuracy with high compute efficiency. Developers can now explore and run this model on Bitdeer AI’s high performance NVIDIA GPU infrastructure, making it easier to experiment, deploy, and scale demanding AI workloads.

What is NVIDIA Nemotron 3 Super

NVIDIA Nemotron 3 Super, part of the Nemotron 3 family of open models, is optimized for complex multi-agent applications. NVIDIA-Nemotron-3-Super-120B-A12B is a 120B-parameter model built on a hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture, activating only 12B parameters during inference. This design delivers strong performance on complex reasoning tasks while remaining efficient enough for production-scale deployment.

What makes this model particularly notable is its focus on agentic workloads. Rather than being optimized only for general chat, Nemotron 3 Super is built for scenarios where models need to reason across long contexts, call tools accurately, and support multi-step task execution.

Key Specifications:

• Model Size: 120B total parameters with 12B active parameters during inference.
• Architecture: Hybrid Mamba-2 + SSM + Transformer with Mixture-of-Experts (MoE) routing.
• Context Length: Supports up to 1M tokens for long-context reasoning.
• Accuracy: Leading accuracy on the Artificial Analysis Intelligence Index within its model size category.
• Minimum GPU Requirement: 2× H100-80GB
• Multilingual Support: English, French, German, Italian, Japanese, Spanish, Chinese

Key architecture innovations

Agentic AI systems require models capable of advanced reasoning, coding, and long-context analysis, while remaining efficient enough to operate continuously at production scale.

However, multi-agent workflows introduce additional complexity. Compared with traditional chat interactions, these systems generate significantly more tokens as agents repeatedly exchange conversation history, tool outputs, and intermediate reasoning steps across multiple turns. Over long tasks, this expanded context can increase inference costs and introduce challenges such as communication overhead and context drift.

To address these requirements, Nemotron 3 Super is built around several key architectural design principles.

• First, the hybrid Mamba-Transformer architecture improves the model’s ability to process long sequences efficiently while retaining the reasoning strengths of transformer layers.
• Second, the Mixture-of-Experts routing mechanism dynamically selects specialized expert networks for each task, enabling higher performance without proportionally increasing inference cost.
• Third, the model is designed as a fully open model stack, including open weights, training datasets, and training recipes. This allows developers to customize and fine-tune the model on their own infrastructure while maintaining control over data privacy and security.

A layer pattern diagram showing repeating blocks of Mamba-2/MoE pairs interleaved with attention layers

Source: NVIDIA

Together, these capabilities make Nemotron 3 Super particularly well suited for building complex multi-agent systems and enterprise AI workflows. And Nemotron 3 Super is optimized for tool calling, multi-step reasoning, and agent orchestration. These capabilities are essential for emerging AI agent systems, where models must interact with external tools, APIs, and data sources to complete tasks.

Benchmark performance

Nemotron 3 Super delivers leading accuracy across several agentic and reasoning benchmarks while maintaining high throughput efficiency. The model demonstrates strong capabilities in instruction following, coding, tool use, and long-context reasoning.

The benchmark results below highlight its performance across key agentic evaluation tasks.

A chart comparing Nemotron 3 Super accuracy on key benchmarks against similarly sized open models.

Source: NVIDIA 

Enterprise use cases

Nemotron 3 Super is designed for complex enterprise AI applications, particularly those that involve multi-agent reasoning and large knowledge contexts. These capabilities make the model well suited for a variety of industry use cases where advanced reasoning and scalable AI workflows are required. Below are several representative examples： 

Software Development: 

With strong coding and tool-calling capabilities, as well as the ability to work across large codebases, Nemotron 3 Super can support end-to-end software development workflows, including code generation, automated debugging, and testing.

Deep Research and Search: 

Nemotron 3 Super has demonstrated strong performance in enterprise research workflows, with capabilities well suited for comprehensive report generation and precise factual recall. This makes it a strong fit for literature review, competitive intelligence, and research automation.

Cybersecurity: High-accuracy tool calling enables autonomous agents to select the appropriate tools more reliably, reducing execution errors in high-stakes environments such as security operations and automated cybersecurity workflows.

Financial Services: 

Nemotron 3 Super can process large volumes of financial reports and long-context documents in a single workflow, helping analyst agents maintain context over extended tasks and improving both efficiency and analytical accuracy.

Run Nemotron 3 Super via API on Bitdeer AI Model Studio

Bitdeer AI Model Studio is a serverless inference platform where you can immediately access and run foundation models like Nemotron 3 Super through a simple API. 

Step 1: Select the Model

Log in to Bitdeer AI Model Studio , locate NVIDIA-Nemotron-3-Super-120B-A12B in the model list, and start using it for your AI workloads, priced at  $0.30/M in ·$0.80/M out. 

Step 2: Generate an API Key

Before using the model, you need an API key. Go to API Keys in the left navigation panel and click Generate API Key. 

Step 3: Call the Model via API

Once the API key is created, you can call the model using the Bitdeer AI inference API by including your API key in the request header.

With Model Studio and API access, developers can quickly start experimenting with Nemotron 3 Super and integrate the model into their AI applications.

Conclusion

With strong reasoning capabilities, long-context support, and an efficient hybrid architecture, NVIDIA-Nemotron-3-Super-120B-A12B provides a powerful foundation for building advanced AI applications. From complex reasoning and coding tasks to large-scale knowledge processing and agentic workflows, the model enables developers to tackle demanding workloads with greater efficiency and flexibility. 

Ready to experience NVIDIA-Nemotron-3-Super-120B-A12B in action? Explore the model on Bitdeer AI Model Studio and see how it can accelerate your next AI applications.

OpenClaw is a powerful AI agent framework that enables you to build intelligent assistants with multiple capabilities, including web search, code execution, and integration with various AI models. With Bitdeer AI Cloud, OpenClaw can be deployed online with flexible resource allocation and usage-based pricing, making it easier to run AI agents without long-term hardware constraints.

In this guide, we'll walk through the complete process of deploying OpenClaw on Bitdeer AI Cloud and configuring it to work with Telegram for an interactive AI assistant experience.

Prerequisites

Before getting started, ensure you have:

• A Bitdeer AI Cloud account
• Basic knowledge of Linux command line
• A Telegram account for bot integration

Step 1: Setting Up Bitdeer AI Cloud Instance

Ordering a Virtual Machine

First, navigate to the Bitdeer AI Cloud platform and place an order for a virtual machine instance.

Select an appropriate instance configuration based on your needs. For OpenClaw, we recommend:

• CPU: At least 2 cores
• RAM: Minimum 4GB
• Storage: At least 20GB SSD
• OS: Ubuntu 20.04 or later

Once your order is confirmed, wait for the instance to be provisioned.

Accessing Your Instance

After the instance is ready, you'll see it in your dashboard with a "Running" status.

Connect to your instance using SSH:

ssh username@your-instance-ip

Step 2: Installing OpenClaw

Quick Installation

OpenClaw provides a simple one-line installation script that handles all dependencies automatically:

curl -fsSL https://openclaw.ai/install.sh | bash -s -- --no-onboard

This command will:

• Install all required system dependencies
• Set up Node.js environment
• Install OpenClaw

Once the installation is completed, verify it:

openclaw --version

💡 Tip: If you encounter openclaw: command not found, you may need to source your .bashrc file for the current session or update your PATH:

source ~/.bashrc
# Or manually add to PATH if necessary
export PATH="$HOME/.npm-global/bin:$PATH""

Step 3: Configuring OpenClaw

Initial Setup

Run the OpenClaw setup wizard to initialize your environment:

openclaw setup

This will create the basic configuration structure for OpenClaw.

Configuring AI Models

After the setup completes, edit the OpenClaw configuration file to add your AI model settings:

vim ~/.openclaw/openclaw.json

Update the configuration with your preferred models:

{
  "agents": {
    "defaults": {
      "model": {
        "primary": "bitdeerai/moonshotai/Kimi-K2.5"
      },
      "workspace": "/home/ubuntu/.openclaw/workspace"
    }
  },
  "models": {
    "mode": "merge",
    "providers": {
      "bitdeerai": {
        "baseUrl": "https://api-inference.bitdeer.ai/v1",
        "apiKey": "your-api-key-here",
        "api": "openai-completions",
        "models": [
          {
            "id": "moonshotai/Kimi-K2.5",
            "name": "Kimi-K2.5"
          }
        ]
      }
    }
  }
}

You can find more details in model card here:

Getting Bitdeer AI Cloud API Key

To obtain your API key from Bitdeer AI Cloud:

1. Log in to your Bitdeer AI Cloud account
2. Navigate to the API Keys section in Models
3. Click "Generate API Key"
4. Copy the generated API key and save it securely

⚠️ Important: Keep your API keys secure and never commit them to version control.

Verify Model Configuration

After configuring your models, verify that they are properly set up by listing available models:

openclaw models list

This command will display configured models and their availability.

Onboarding Process

Complete the onboarding process to finalize your OpenClaw service:

openclaw onboard --flow quickstart

Setting Up Telegram Channel

During the onboarding process, select Telegram as your communication channel and provide your bot token.

After completing the onboarding process, the OpenClaw service will automatically start and run in the background.

Accessing OpenClaw Control Panel (Optional)

If you want to access the OpenClaw control panel via web interface, you can set up an SSH tunnel and access it using the gateway token.

First, find your gateway token in the configuration file:

cat ~/.openclaw/openclaw.json

Look for the gateway configuration section:

{
  "gateway": {
    "mode": "local",
    "auth": {
      "mode": "token",
      "token": "your-gateway-token"
    },
    "port": 18789
  }
}

Create an SSH tunnel from your local machine to access the OpenClaw control panel:

# Run this on your local machine
ssh -L 18789:localhost:18789 username@your-instance-ip

Then open your browser and navigate to:

http://localhost:18789?token=your-gateway-token

Use the gateway token from your configuration file to authenticate.

Step 4: Pairing with Telegram

Connect Your Telegram Chat

After the OpenClaw service is running with Telegram integration enabled, you need to pair your Telegram chat:

1. Open Telegram and find your bot
2. Send /start command to initiate the conversation
3. The bot will respond with a pairing code
4. Enter the pairing code in your OpenClaw terminal:

openclaw pairing approve telegram <pairing-code>

Interactive Chat

Once paired, you can start chatting with your AI assistant:

The bot can:

• Answer questions using the configured AI model
• Perform web searches
• Execute code snippets (with proper sandboxing)
• Handle file uploads and processing
• Maintain conversation context

Using Telegram Commands

OpenClaw provides several built-in commands:

Common commands include:

• /start - Initialize the bot
• /help - Show available commands
• /status - Check bot status

Conclusion

You now have a fully functional OpenClaw AI assistant running on Bitdeer AI Cloud with Telegram integration. This setup provides you with a powerful, customizable AI agent that can assist with various tasks while maintaining security and performance.

The combination of Bitdeer AI Cloud's reliable infrastructure and OpenClaw's flexible framework creates an ideal environment for building and deploying intelligent assistants. As you become more familiar with the platform, you can explore advanced features like:

• Advanced agent skills
• Integration with other channels

Happy building!

*Security Note: Apply appropriate access controls for sensitive actions.

Additional Resources

• Bitdeer AI Cloud Documentation
• OpenClaw GitHub Repository

Hack to Hire 2 has concluded earlier, but the use cases developed during the event continue to stand out. Rather than focusing on experimental models or theoretical benchmarks, the hackathon produced concrete system designs that address persistent business challenges across supply chains, retail operations, and document-heavy industries.

Built within a limited timeframe, these projects demonstrated how AI when paired with the right infrastructure and system architecture can be embedded directly into operational workflows. Even after the event, the use cases remain relevant as practical references for how enterprises can translate AI capabilities into real business value. In the following article, we will take a closer look at these prototype cases and further explore their practical applications and value.

Use Case 1: Market & Supplier Intelligence for Sustainable Supply Chains

Background

In the textile manufacturing supply chain, sustainability and supplier quality are no longer secondary concerns. Decision-makers must continuously assess compliance, performance, and risk across a large and evolving supplier base. However, relevant data is often distributed across internal records, supplier submissions, external documents, and public news sources.

Technical & Business Challenges

The primary challenge lies in fragmentation. Supplier information exists in multiple formats and systems, making it difficult to establish a unified, up-to-date view. As a result, sustainability compliance checks and quality assessments are largely manual, delaying critical decisions and increasing operational risk.

Solution Architecture

The proposed solution consolidates supplier data, documents, and external information through a centralized data pipeline. Within the system architecture, Bitdeer AI Cloud provides the virtual machine environment and data processing layer used to run ETL workflows, host the analytical dashboard, and support AI-driven insight generation.

Structured data is stored centrally, while AI models analyze supplier attributes to produce quality scores and contextual recommendations. The dashboard presents these insights in a unified view, enabling more informed and timely supplier management decisions

Outcome

The prototype illustrates how AI-supported analytics can transform supplier evaluation from manual reviews into a more systematic and insight-driven process, particularly valuable for sustainability-focused supply chains.

Use Case 2: Retail Store Intelligence for Planogram Execution

Background

Retail ground staff spend significant time executing product placement according to predefined planograms. Store managers must oversee large teams, while brand and product managers often lack direct visibility into how products are ultimately displayed on the shop floor.

Challenges

Manual reporting and visual checks are time-consuming and inconsistent. Without a structured feedback mechanism, it is difficult to assess execution quality or scale oversight across multiple stores.

Solution Architecture

The system digitizes the planogram workflow from instruction to verification. Product placement requirements are recorded and distributed to ground staff via a centralized interface. After execution, staff upload photos through a mobile web browser.

Within the architecture, Bitdeer AI Cloud hosts the operational dashboard and supports model inference used to analyze uploaded images. The results are automatically recorded and aggregated, allowing managers to review execution status and performance without manual follow-up.

Outcome

By combining visual evidence with automated reporting, the solution improves transparency across retail operations and reduces the manual burden on both frontline staff and management.

Use Case 3: Automated Tender Document Intelligence for SMEs

Background

Singapore’s construction industry issues thousands of government tenders each year, many consisting of hundreds of pages of documentation. For SMEs, participating in these tenders is critical but resource-intensive.

Challenges

Tender documents are typically received via email and reviewed manually. Processing a single tender can take several days, placing heavy strain on small quality assurance teams and increasing the risk of missed deadlines or errors.

Solution Architecture

The proposed pipeline automates tender intake and evaluation. Incoming emails trigger document processing workflows that extract, analyze, and assess tender requirements. AI and Retrieval-Augmented Generation (RAG) techniques reference internal document repositories such as certificates and project histories to perform eligibility checks.

Bitdeer AI Cloud provides the execution environment for these automated workflows, hosting the virtual machines that run document processing, AI inference, and the dashboard used to visualize results and audit pipeline actions

Outcome

The prototype demonstrates how AI-enabled automation can significantly shorten tender review cycles, helping SMEs respond more efficiently and compete more effectively in high-volume tender environments.

Infrastructure as an Enabler, Not the Focus

Across all three use cases, Bitdeer AI Cloud functions as the underlying execution layer that supports data pipelines, AI inference, and dashboard deployment. By providing a consistent and flexible runtime environment, teams were able to focus on workflow design and problem-solving rather than infrastructure setup.

This separation between infrastructure and application logic proved critical in enabling rapid iteration within the hackathon timeframe.

Lasting Takeaways

The solutions developed through Hack to Hire demonstrate more than short term experimentation. They provide concrete examples of how AI can be operationalized when designed around real business workflows rather than isolated models or theoretical prototypes.

Across all use cases, a consistent pattern emerges. Real value is created when AI is embedded into end to end processes and supported by infrastructure that enables rapid iteration and reliable execution. In this context, Bitdeer AI is positioned as a vertically integrated AI cloud platform that unifies AI data center infrastructure with a comprehensive AI cloud layer, providing built in tools that support the full AI lifecycle from development and training to deployment and production operations. Together, these projects illustrate a pragmatic path for enterprises adopting AI by focusing on workflow integration, operational impact, and measurable outcomes.