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NVIDIA VP of Generative AI Kari Briski explains why a chip maker builds LLMs: the hardware-software co-design feedback loop requires deeply understanding workloads to optimize them. She covers NVIDIA's Nemotron model family (Nano, Super, Ultra), the benefits of training at reduced floating-point precision (NVFP4) over post-training quantization, hybrid Mamba State Space + Transformer architectures for token efficiency, mixture-of-experts approaches, and disaggregated inference serving via Dynamo. Nemotron is fully open source—releasing weights, training data, gym environments, and recipes—enabling enterprises to fine-tune on their own domains without liability concerns. The roadmap treats models like software libraries with regular release cycles, and community PRs to model architecture are planned for the future.

Even the chip makers are making LLMs