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As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an optimization technique that compresses model weights into a smaller data format. One quantization format is NVFP4, an innovative 4-bit floating point introduced with NVIDIA Blackwell architecture. That’s the approach behind our new Nemotron 3 Ult

Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right. To address this, the NVFP4 training recipe

RATGEBER **Reducing GPU Costs for AI Inference: FP8, FP4, and vLLM** 3 Juni 2026 8 min read Training costs for a model are one-time, but inference costs accrue every day. That’s where the math is shifting: with native FP4 Tensor Cores on NVIDIA Blackwell and a serving layer like vLLM that leverages these formats, GPU hours and latency can be significantly reduced-without retraining the model. Fo