Introducing the Nemotron-H Reasoning Model Family: Throughput Gains Without Compromise

As large language models increasingly take on reasoning-intensive tasks in areas like math and science, their output lengths are getting significantly…

As large language models increasingly take on reasoning-intensive tasks in areas like math and science, their output lengths are getting significantly longer—sometimes spanning tens of thousands of tokens. This shift makes efficient throughput a critical bottleneck, especially when deploying models in real-world, latency-sensitive environments. To address these challenges and enable the…

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Source:: NVIDIA