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Accelerating a ROS 2 Node with an AI Agent and NVIDIA Isaac ROS

GPU acceleration can speed up compute-intensive robotics workloads, but a fast CUDA kernel alone does not guarantee a fast ROS 2 graph. As messages move between…

GPU acceleration can speed up compute-intensive robotics workloads, but a fast CUDA kernel alone does not guarantee a fast ROS 2 graph. As messages move between nodes, they may continue to be serialized or copied through CPU memory, eroding the benefits of keeping perception and AI workloads on the GPU (Figure 1). With the upstream abstraction and the CUDA buffer backend that NVIDIA recently…

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Transformers now runs llama.cpp quants

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Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community

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How to Evaluate AI Agents From Tool Calls to Task Completion

When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and…

When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment, and recover when a step fails. Scoring whether the model sounds right tells you almost nothing about whether the work finished. That gap is why agent evaluation has had to evolve from scoring a single function call to scoring an entire task…

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Simplifying Model Serving Across Multiple GPUs with NVIDIA TensorRT Multi-Device Integration in NVIDIA Dynamo-Triton

The compute and memory demands of generative AI increasingly exceed what a single GPU can provide. NVIDIA TensorRT multi-device inference is a new capability…

The compute and memory demands of generative AI increasingly exceed what a single GPU can provide. NVIDIA TensorRT multi-device inference is a new capability that enables a single TensorRT network to execute across multiple GPUs using NCCL-backed distributed collectives while retaining TensorRT inference optimizations. It is fully supported starting with TensorRT 11.0.

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NVIDIA Launches DSX Ready to Qualify Power and Cooling Products for AI Factories

Every AI factory needs power and cooling that fit its computing architecture. As AI infrastructure expands, power, cooling, water, site and grid constraints are shaping what builders can deploy. Choosing products that fit the complete factory design helps builders turn computing capacity into useful AI output. To help builders make those decisions, NVIDIA is introducing […]

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Why Deploying Physical AI at Scale Demands Safety at Every Layer

Physical AI is moving rapidly from research to large-scale deployment. By 2035, ABI Research projects an installed base of 49 million level 3-5 autonomous vehicles (AVs), while Omdia estimates that roughly 60 million industrial robots will be deployed between 2026 and 2035. As these machines enter roads, factories, warehouses and other environments shared with people, […]

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From Enablement to Execution, Egypt’s AI Ecosystem Reaches Production Scale

Today, Egypt’s AI builders gathered in the Grand Egyptian Museum for a reception that highlighted the nation’s rapidly growing AI ecosystem — spanning AI natives, developers, researchers, startups and enterprises — building applications across industries. The event included a keynote from Paolo Guglielmini, vice president of EMEA at NVIDIA. Ahmed Mostafa, regional AI adoption lead […]

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Turn Your Latest Observations Into Timely Weather Decisions With NVIDIA Earth-2

Weather-sensitive industries increasingly have access to observations that offer an earlier, more local view of changing conditions. Energy companies collect…

Weather-sensitive industries increasingly have access to observations that offer an earlier, more local view of changing conditions. Energy companies collect measurements across wind and solar assets, emergency management teams rely on radar and local sensors, and satellite providers continuously observe the Earth. This data helps organizations understand and manage physical risk across sectors…

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AI Security Is an Engineering Problem — How to Solve It at Every Layer of the Agent Stack

AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work.  As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster.  Technology Changes, Security Fundamentals Endure The internet and cloud computing changed how software operates, […]