As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with...

As AI models continue to get smarter, people can rely on them for an expanding set of tasks. This leads users—from consumers to enterprises—to interact with AI more frequently, meaning that more tokens need to be generated. To serve these tokens at the lowest possible cost, AI platforms need to deliver the best possible token throughput per watt. Through extreme co-design across GPUs, CPUs…

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AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter...

AI innovation continues to be driven by three scaling laws: pre-training, post-training, and test-time scaling. Training is foundational to building smarter models, and post-training—which can include fine-tuning, reinforcement learning, and other techniques—helps to further increase accuracy for specific tasks, as well as provide models with new capabilities like the ability to reason.

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The NVIDIA Blackwell architecture powered the fastest time to train across every MLPerf Training v5.1 benchmark, marking a clean sweep in the latest round of...

The NVIDIA Blackwell architecture powered the fastest time to train across every MLPerf Training v5.1 benchmark, marking a clean sweep in the latest round of results. As developers experiment with new architectures, and models continue to grow in size, more training compute is essential. Meeting this need for delivered compute requires innovation across every layer of the AI stack—from chips and…

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