{"ticker":"NVDA","query":null,"summary":"NVDA has 8 indexed evidence chunks from NVIDIA Developer Blog RSS, NVIDIA Press Room RSS. Latest source: NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents.","evidence":[{"id":"8a122989-0cc3-4b26-85da-e051de56e604","sourceItemId":"47861628-39b7-41f8-a4a4-c40e347ab77b","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/","sourceTitle":"NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents","publisher":"NVIDIA Developer Blog RSS","chunkText":"NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete... Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete complex workflows. However, these multi-agent workflows cause token counts to grow quickly. Agents plan, call tools, invoke sub-agents, receive information, and then pass history, outputs, and reasoning steps back into the model… Source","publishedAt":"2026-06-04T13:02:49+00:00"},{"id":"3db239e3-76fb-432c-ae54-5ff672172be9","sourceItemId":"934d632f-b1d7-46cd-a819-e149eb9a6468","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/run-local-ai-agents-with-faster-models-and-multi-node-clustering-on-nvidia-dgx-spark/","sourceTitle":"Run Local AI Agents with Faster Models and Multi-Node Clustering on NVIDIA DGX Spark","publisher":"NVIDIA Developer Blog RSS","chunkText":"Run Local AI Agents with Faster Models and Multi-Node Clustering on NVIDIA DGX Spark Run Local AI Agents with Faster Models and Multi-Node Clustering on NVIDIA DGX Spark The rise of autonomous, long-running AI agents has introduced a new class of compute demand, namely tasks that maintain large context windows, spawn concurrent... The rise of autonomous, long-running AI agents has introduced a new class of compute demand, namely tasks that maintain large context windows, spawn concurrent subagents, and iterate continuously without cloud dependency. Security and privacy concerns are also accelerating the shift toward local agents. Developers, by running autonomous agents on hardware they own with NVIDIA NemoClaw… Source","publishedAt":"2026-06-01T22:00:00+00:00"},{"id":"da916a4d-54b1-4266-a0d7-0f66bbe47258","sourceItemId":"9c61015a-698f-4eff-92a5-796476d8ddcb","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/delivering-lifecycle-control-for-ai-infrastructure-at-scale-with-nvidia-dgx-spark-enterprise-manageability/","sourceTitle":"Delivering Lifecycle Control for AI Infrastructure at Scale with NVIDIA DGX Spark Enterprise Manageability","publisher":"NVIDIA Developer Blog RSS","chunkText":"Delivering Lifecycle Control for AI Infrastructure at Scale with NVIDIA DGX Spark Enterprise Manageability Delivering Lifecycle Control for AI Infrastructure at Scale with NVIDIA DGX Spark Enterprise Manageability As AI infrastructure scales, enterprise expectations for operational maturity are increasing. Organizations expect these systems to be provisionable,... As AI infrastructure scales, enterprise expectations for operational maturity are increasing. Organizations expect these systems to be provisionable, observable, secure, and manageable at scale—the same standard applied to all critical infrastructure. The moment an AI system moves from development into enterprise deployment, that operational foundation is essential. NVIDIA DGX Spark and… Source","publishedAt":"2026-06-09T19:00:00+00:00"},{"id":"c9e89e6e-8c09-4b2f-8b1b-3bc81a7489c6","sourceItemId":"79e9226c-c69a-45b6-ab62-0c235b6d3394","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/model-quantization-turn-fp8-checkpoints-into-high-performance-inference-engines-with-nvidia-tensorrt/","sourceTitle":"Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT","publisher":"NVIDIA Developer Blog RSS","chunkText":"Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster... Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput, and more efficient GPU utilization at scale. In a previous post, we produced a high-quality FP8-quantized Contrastive Language-Image Pretraining (CLIP) checkpoint with NVIDIA TensorRT Model Optimizer. Source","publishedAt":"2026-06-09T18:27:52+00:00"},{"id":"f2dd85b6-6957-4e56-8251-fc9fbebe4750","sourceItemId":"2809fdef-a2fc-4729-ba36-05035926c5f4","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/accelerating-federated-learning-research-with-ai-agents-and-nvidia-flare-auto-fl/","sourceTitle":"Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL","publisher":"NVIDIA Developer Blog RSS","chunkText":"Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a... Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric? Source","publishedAt":"2026-06-09T16:35:08+00:00"},{"id":"feeac0fb-1a00-4e48-a6cd-a993bf8e0a33","sourceItemId":"b34e974a-00ba-4cde-b8d6-0c5d36718e47","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/evaluate-clinical-asr-models-faster-with-agent-skills-and-nvidia-nemotron-speech/","sourceTitle":"Evaluate Clinical ASR Models Faster with Agent Skills and NVIDIA Nemotron Speech","publisher":"NVIDIA Developer Blog RSS","chunkText":"Evaluate Clinical ASR Models Faster with Agent Skills and NVIDIA Nemotron Speech Evaluate Clinical ASR Models Faster with Agent Skills and NVIDIA Nemotron Speech Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine,... Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine, Cefazolin, and Biktarvy are not part of everyday vocabulary. Procedure names, anatomy terms, and specialty-specific diagnoses introduce the same problem in a different form. Off-the-shelf speech systems can sound fluent and still miss the words… Source","publishedAt":"2026-06-09T15:00:00+00:00"},{"id":"7b4189e1-496e-4fb4-adc8-ee6d8ad0e541","sourceItemId":"e9521ca9-b8db-459e-8938-bd678ddf074a","ticker":"NVDA","sourceType":"rss","sourceUrl":"https://developer.nvidia.com/blog/train-models-faster-with-jax-and-maxtext-using-nvfp4-on-nvidia-blackwell/","sourceTitle":"Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell","publisher":"NVIDIA Developer Blog RSS","chunkText":"Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step... Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right. To address this… Source","publishedAt":"2026-06-08T18:18:06+00:00"},{"id":"41374863-b673-4660-b659-5643713ab577","sourceItemId":"7233cf74-cfc9-4381-8de1-1c093826b091","ticker":"NVDA","sourceType":"company_press_release","sourceUrl":"https://blogs.nvidia.com/blog/uk-sovereign-ai-advancements/","sourceTitle":"How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies","publisher":"NVIDIA Press Room RSS","chunkText":"How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies How the UK Is Turning Sovereign AI Ambition Into Action With NVIDIA Technologies A year ago at London Tech Week, NVIDIA founder and CEO Jensen Huang and U.K. Prime Minister Keir Starmer made a declaration: the U.K. would be an AI maker, not an AI taker. At this year’s event, NVIDIA and its partners are showcasing how that commitment is producing real momentum across the nation’s infrastructure, startups […]","publishedAt":"2026-06-08T06:00:57+00:00"}]}