Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states,...

Large language model (LLM) training workloads increasingly run into GPU memory limits before compute is fully used. Model weights, gradients, optimizer states, communication buffers, and intermediate activations all compete for GPU high-bandwidth memory (HBM). As model size, sequence length, and batch size grow, HBM capacity often becomes the primary scaling bottleneck. This post explains how…

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AI performance comes down to three dimensions:  Accuracy: How well the model reasons and produces outputs Throughput: How many tokens per second a...

AI performance comes down to three dimensions: Deployments must balance all three: High accuracy is wasted if responses are slow, and raw throughput means little if each user’s experience is laggy. Practical systems therefore optimize accuracy, throughput, and interactivity together. This post focuses on throughput and interactivity, and how model-design choices shape both without…

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Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein...

Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein design. Increasingly, they’re driven end-to-end by AI agents. For an agent to run that pipeline well, every step needs to be fast and scalable: Multiple Sequence Alignment (MSA) generation, co-folding inference, serving, and multi-GPU scale-out.

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Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings...

Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings and stock movements, while rarer events such as credit-rating changes, product approvals, and labor issues are harder to capture at scale. Synthetic generation can help fill those gaps for trading research, risk modeling, and surveillance…

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Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are...

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents. However, fine-tuning requires expertise and hardware for…

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Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context,...

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context, determines the correct procedure, checks whether a specialist signal confirms the failure mode, and writes up a recommendation. This process remains consistent, and is well-suited for an AI agent. This post discusses a per-alarm…

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Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and...

Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and result handling. As these systems scale across the AI factory, performance depends not only on GPU acceleration, but also on the CPU work that happens between model steps. Across the creation and deployment of an agentic system…

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AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns...

AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns surrounding data privacy, sovereignty and how to secure data while it is in use, or during inference and engagement with AI models. NVIDIA Confidential Computing (CC) was engineered to be a secure and performant solution for the era of agentic…

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Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF) within AI assistants to newer...

Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF) within AI assistants to newer reinforcement learning with verifiable rewards (RLVR) workflows for reasoning and agent tasks. RL is now becoming a practical technique for specialized AI where enterprises need more accurate agents for domain-specific workflows.

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AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on...

AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on behalf of a user. This unlocks productivity, but can also give agents access to sensitive enterprise data and the ability to complete tasks and take action across business systems, making a secure, governed environment essential.

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AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep...

AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep some context across a session. Today, we have long-horizon agents. Systems that plan many steps, split work between sub-agents, keep context across a long task, and run tools in a safe sandbox. The NVIDIA AI-Q Blueprint is an open source…

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As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an...

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an optimization technique that compresses model weights into a smaller data format. One quantization format is NVFP4, an innovative 4-bit floating point introduced with NVIDIA Blackwell architecture. That’s the approach behind our new Nemotron 3…

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Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the...

Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the challenge is scaling across multiple devices without sacrificing the critical optimizations—like kernel fusions, memory planning, and quantization—that NVIDIA TensorRT delivers for production deployments. Multi-device inference support…

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As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs...

As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs generate tokens sequentially, which can limit GPU utilization and constrain throughput in latency-sensitive serving scenarios. Speculative decoding helps mitigate this bottleneck by using a lightweight model to draft future tokens…

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Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In...

Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In network operations, for example, automation typically sits in the Level 2–3 band of TM Forum’s autonomous networks levels taxonomy, streamlining execution of predefined solutions in selective network domains. Reaching Level 4–5 autonomy…

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When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...

When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971 and preserved in the Protein Data Bank. Measured data is the backbone for all AI models and workflows that process data as it’s created, act on what matters in real time, and analyzes data for deep insights. With the current rise of modern…

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Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI…

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Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live...

Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live camera and microphone streams, multimodal AI models, enterprise data, tool use, deployment infrastructure, and device-specific runtimes. NVIDIA XR AI is designed to address this challenge by providing a reusable foundation for…

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NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium....

NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium. NVIDIA achieved the fastest time to train at scale, and also delivered the highest performance when normalized on a per-accelerator basis on every benchmark. It was also the only platform to submit on every test.

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Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable...

Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable substantially larger model capacity while activating only a subset of parameters for each token, offering an unparalleled approach for scaling performance within a practical compute budget. As model scales continue to grow…

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