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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Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large...

Agentic AI systems increasingly rely on collections of cooperating agents—retrievers, planners, tool executors, verifiers—working together across large contexts and long time spans. These systems demand models that deliver fast throughput, strong reasoning accuracy, and persistent coherence over large inputs. They also require a level of openness that allows developers to customize, extend…

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Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety...

Validating AI systems requires benchmarks—datasets and evaluation workflows that mimic real-world conditions—to measure accuracy, reliability, and safety before deployment. Without them, you’re guessing. But in regulated domains such as healthcare, finance, and government, data scarcity and privacy constraints make building benchmarks incredibly difficult. Real-world data is locked behind…

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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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NVIDIA researchers on Friday won a key Kaggle competition many in the field treat as a real-time pulse check on humanity’s progress toward artificial general...

NVIDIA researchers on Friday won a key Kaggle competition many in the field treat as a real-time pulse check on humanity’s progress toward artificial general intelligence (AGI). Ivan Sorokin and Jean-Francois Puget, two members of the Kaggle Grandmasters of NVIDIA (KGMoN), came in first on the Kaggle ARC Prize 2025 public leaderboard with a 27.64% score by building a solution evaluated on…

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