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NVIDIA

40 summarised stories about NVIDIA, each linking back to the original source. Browse all topics →

Tuesday, 7 July 2026

AI Innovators Adopt NVIDIA Vera — Why Max Single-Threaded CPU at Scale Matters

NVIDIA 1 week ago

NVIDIA introduced Vera, a CPU designed specifically for agentic AI systems that prioritizes single-threaded performance to execute tool calls and data processing between model calls. Vera delivers 1.8x higher sustained per-core performance than x86 CPUs in agentic workloads, with Perplexity achieving 1.5x faster performance on real coding workflows. This optimized CPU architecture helps AI factories reduce GPU idle time and complete more agent tasks by ensuring each step in the agent loop runs faster.

Nvidia's next-gen AI rack system delayed to 2028 on manufacturing snags

TLDR 1 week ago

Nvidia's Kyber rack system, designed to house its 2027 Rubin Ultra chips in a single cabinet with 144 processors, has been delayed to 2028 due to manufacturing difficulties with a specialized circuit board. The delay pushes back the system's original 2027 launch by more than 12 months, and a proposed backup solution using two current-generation racks was scrapped after cloud providers rejected it as operationally impractical. The setback leaves Nvidia without a proven way to scale up the Rubin Ultra system to larger configurations, potentially opening a market opportunity for AMD and Google's custom chips in high-end AI infrastructure.

NVIDIA and Hugging Face Bring New Models and Frameworks to LeRobot for the Open Robotics Community

NVIDIA 1 week ago

NVIDIA and Hugging Face integrated NVIDIA's Isaac GR00T 1.7 vision-language-action model and Isaac Teleop framework into LeRobot, an open source robotics library, with NVIDIA Cosmos 3 planned for future addition. The integration connects NVIDIA's 3 million robotics developers with Hugging Face's 16 million AI builders and provides access to datasets containing over 350,000 trajectories and 57 million grasps. Developers can now use standardized workflows to collect data, train robot foundation models, and deploy them across different robot embodiments with benchmarked performance validation.