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Misc

How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories

For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster…

For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster must synchronize gradients across thousands of collective operations per second. Similarly, during inference, unplanned downtime directly reduces the total volume of requests served, strictly limiting revenue generation.

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Misc

Your Agent Aced the Task. Will It Do It Again?

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Misc

Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE

Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the…

Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the challenge shifts from running an algorithm to operating shared infrastructure. GPUs must be allocated when jobs need them, multiple research studies must remain separated, and every participating organization must retain control of its own…

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Heart of the Matter: How a Major Children’s Hospital Uses Open Source NVIDIA AI for Cardiac Care

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Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE…

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE models that match or exceed the performance of dense model counterparts at a fraction of the training compute. MoE models provide efficient training through conditional computation. Instead of one dense feed-forward network (FFN) shared…

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Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX

As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information […]

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NVIDIA Expands Open Source CUDA-Q Platform for Fault-Tolerant Quantum Computing

NVIDIA today announced an expansion of the NVIDIA CUDA-Q™ open source platform with CUDA-Q Logical, an orchestration layer that provides a programmable, verifiable approach to developing useful applications for fault-tolerant quantum computers.

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Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL

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Misc

Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies

The global robotaxi market — physical AI’s first commercial breakthrough — is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world’s busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is […]

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Misc

Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video

Manufacturing floors, warehouses and production lines rarely stay fixed — tasks change, layouts shift and new products arrive, and most robots can’t keep up without significant reprogramming. Skild AI’s new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses […]