AI Applications

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

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

The rapid advancements in artificial intelligence require cutting-edge hardware capable of handling the immense computational demands of machine learning , deep learning , and AI-driven workloads. One of the most anticipated breakthroughs in this domain is the NVIDIA GB200 NVL72, a GPU designed specifically to meet the needs of high-performance computing and

In the era of data-driven applications and AI-powered innovations, businesses require reliable, scalable, and high-performance database solutions. To meet this growing demand, we are excited to showcase RDS (Relational Database Service) on our AI cloud platform. Designed to simplify database management while ensuring security and scalability, our RDS solution is

AI is becoming essential to the financial industry’s broader digital transformation goals. As data volumes rise and customer expectations shift toward immediacy, precision, and always-on service, financial institutions are recognizing AI’s potential to improve decision-making, strengthen operations, and increase the speed and quality of customer engagement. With tools,

The Hack to Hire hackathon series, jointly organized by Bitdeer AI and GECO Asia concluded the first event earlier in September, bringing together emerging engineers, developers, and data scientists to tackle real-world business challenges through AI. Over several days, participants used Bitdeer AI Cloud’s GPU virtual machines and AI

GPUs have become the backbone of modern Artificial Intelligence (AI), High-Performance Computing (HPC), and Generative AI. NVIDIA has played a pivotal role in this transformation, evolving from a graphics accelerator vendor into the enabler of factory-scale AI platforms. Each architectural generation has been driven by one principle: resolve systemic bottlenecks

AI is evolving beyond passive text completion; It is stepping into autonomy. In this new era, systems do not just respond, they think, plan, coordinate, and act on their own. This is the realm of agentic AI. And at its core lies a set of reusable design blueprints: agentic patterns.

As language models become increasingly central to modern AI applications, particularly within agentic systems, the ongoing pursuit of ever-larger models is prompting a new question: could smaller models provide a smarter and more efficient alternative? While Large Language Models (LLMs) are widely praised for their broad capabilities, growing evidence suggests

Much of the AI narrative today revolves around consumer tools or futuristic fears. In reality, the technology is already reshaping everyday business workflows.In the real world, advanced software is already working quietly behind the scenes — answering customer queries, booking appointments, summarizing reports, flagging anomalies, and even automating parts of

As artificial intelligence transitions from prompt-based tools to autonomous systems, the need for scalable and modular infrastructure becomes essential. NVIDIA Inference Microservices (NIMs) offer a streamlined solution to this challenge, enabling developers and enterprises to deploy powerful open-source foundation models quickly and reliably. Bitdeer AI is an officially licensed partner

In the fast-evolving landscape of AI-driven innovation, businesses are turning to neocloud providers to power their computational needs. Unlike traditional hyperscalers like AWS, Azure, or Google Cloud, neocloud platforms specialize in GPU-as-a-Service (GPUaaS), offering tailored infrastructure for AI and machine learning workloads. With a focus on cost efficiency, high performance,

Imagine rolling out a customer-service chatbot that, within minutes, can switch from handling billing inquiries to troubleshooting technical issues without rebuilding its entire knowledge base. Or picture a warehouse robot that, after a handful of interactions, masters a new assembly line configuration without weeks of retraining. That’s the promise

In a rapidly evolving AI landscape, businesses are under growing pressure to deploy agents that can understand and respond across text, vision, and audio channels. Yet collecting, annotating, and curating real-world multimodal datasets is often costly, labor-intensive, with compliance hurdles. Synthetic data offers a compelling alternative, enabling businesses to accelerate

The earliest chatbot models were goldfish: every new prompt wiped their memory clean; 2025 is the year they finally start acting more like elephants. By fusing large‑context language models with external memory stores, developers can now build stateful agents that remember users, projects and decisions for days, or even

Building successful AI solutions isn’t just about powerful models, it’s also about how those models and processes are organized. The promise of AI, streamlined operations, predictive insights, and hyper-personalized customer experiences, is undeniable. But the reality? Many organizations are grappling with rigid, siloed AI systems that are tough

Artificial intelligence has come a long way since its early days. Today, we’re witnessing a convergence of two seemingly distinct paradigms, symbolic reasoning and subsymbolic learning. Symbolic AI, with its roots in logic and rule-based systems, offers clarity and explicit reasoning. In contrast, subsymbolic methods, such as deep neural

Artificial Intelligence (AI) is rapidly evolving from narrow, single-task models to more sophisticated systems capable of perceiving and reasoning across multiple types of inputs. This evolution is embodied in multimodal AI, a powerful class of AI that understands and synthesizes information from diverse data types like text, images, audio, video,

In our previous blog, we introduced the fundamentals of Retrieval-Augmented Generation (RAG)—a hybrid architecture that combines the strengths of large language models (LLMs) with retrieval mechanisms to enhance factual accuracy and ground responses in external knowledge. While the basic RAG framework opens the door to powerful AI applications, production-grade

As artificial intelligence continues to evolve rapidly, the need for better coordination, interoperability, and contextual understanding among models has never been more urgent. Enter the Model Context Protocol (MCP), an emerging specification that has quickly gained traction in AI communities and tech giants alike. By early 2025, MCP has become

The open-source AI landscape has seen a significant advancement with the release of Llama 4 by Meta on April 5. This latest iteration of Meta's powerful large language models (LLMs) brings substantial improvements in reasoning, instruction-following, and overall performance, marking the most notable upgrade in the Llama LLM