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Nvidia announced full-scale production of its Spectrum-X Ethernet Photonics co-packaged optical switch for AI factories, using 200G-per-lane links. SK hynix simultaneously published a CPO technology roadmap targeting over 100 Tb/s per node, sub-1 pJ/bit energy consumption, and sub-10 ns chip-to-chip latency.
NVIDIA introduces Cosmos 3 Edge, a compact world model designed for on-device robot control. The 4B omni-model includes a 2B NVIDIA Nemotron-based reasoner to support adaptive policies across sensors, environments, and tasks.
China has reportedly allowed ByteDance and Tencent to import 10,000 NVIDIA H200 chips each, according to the Financial Times. Other Chinese companies could receive similar approval, potentially easing access to advanced AI computing hardware.
KernelArc is a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. On NVIDIA H100 and B200 GPUs, its implementations ranked first on representative L1, L2, Quantization, and FlashInfer tasks in the July 30, 2026 SOL-ExecBench snapshot.
Generative recommenders are shifting recommendation objectives from embedding similarity toward predicting a user's next action or item. The approach uses user-history sequences and large catalogs to rethink how recommendation systems are trained and served at scale.
Waymo has developed a custom chip designed to improve the performance of its robotaxis. The chip also reduces the company’s reliance on third-party suppliers such as Nvidia.
Chinese AI companies are optimising software to handle growing inference demand while access to high-end Nvidia processors remains limited. Domestic chips can support some inference workloads, but complex coding tasks still depend partly on scarce Nvidia capacity.
NVIDIA demonstrates how developers can build real-time edge AI applications with Holoscan using its CLI, skills, documentation, and AI coding agents. The workflow leverages HoloHub’s reference applications and reusable components to help agents assist with development.
NVIDIA FLARE enables organizations to coordinate training for vision-language models while keeping sensitive image and text data at local institutions. The approach supports multimodal use cases such as visual question answering, captioning, and image-text reasoning without centralizing raw records.
NVIDIA SkillEvaluator is designed to measure whether packaged agent skills improve AI agent performance. It evaluates the value of instructions, examples, and tool guidance that help agents find relevant context, reduce wasted steps, and complete specialized tasks more effectively.