Editorial illustration for NVIDIA's NVFP4 Training Recipe Boosts AI Speed and Cuts Costs
NVFP4: 4-Bit AI Training Breakthrough Cuts Model Costs
NVIDIA's NVFP4 Training Recipe Boosts AI Speed and Cuts Costs
Why does this matter now? Companies racing to scale large language models have hit a familiar wall: training costs spiral while hardware efficiency lags behind inference gains. While the NVFP4 accelerator has already proven its worth in inference workloads, the gap in training performance has left developers juggling expensive clusters and long turnaround times.
Here’s the thing: NVIDIA’s latest move targets that exact bottleneck. By publishing a dedicated training recipe, the firm translates the accelerator’s raw speed into a usable workflow for model builders. Early results, captured in the newest MLPerf Training benchmark run, show multiple GB300 NVL72 systems—five in total—delivering measurable improvements.
The implication is clear: if the recipe lives up to its promise, teams could see faster epochs and lower electricity bills, reshaping how quickly new AI capabilities reach production. The details follow.
NVIDIA also recently published an NVFP4 training recipe, bringing the significant performance benefits of NVFP4 to model training, enabling model makers to train AI faster and at lower cost. In the latest version of the MLPerf Training benchmark suite, multiple NVIDIA GB300 NVL72 systems--totaling 512 Blackwell Ultra GPUs--worked together using NVFP4 precision to complete the Llama 3.1 405B pre-training benchmark in 64.6 minutes. This is 1.9x faster than 512 Blackwell GPUs across multiple NVIDIA GB200 NVL72 systems, which were able to complete the benchmark using FP8 in the prior round. Looking ahead, the NVIDIA Rubin platform delivers large leaps in NVFP4 capability for training and inference, offering 35 petaFLOPS of NVFP4 training compute, and 50 petaFLOPs of NVFP4 Transformer Engine inference compute.
NVIDIA’s NVFP4 training recipe promises a noticeable lift in throughput while trimming the electricity bill attached to large‑scale model development. By pairing lower‑precision formats with a tightly integrated hardware‑software stack, the company claims that developers can push more data through the same silicon in less time. The approach rests on what NVIDIA calls “extreme codesign,” a practice of aligning chip architecture, firmware and libraries to squeeze generational gains from each new platform.
In the most recent MLPerf Training benchmark, a cluster of GB300 NVL72 systems demonstrated the claimed speedups, suggesting that the recipe can deliver measurable benefits under standardized testing conditions. Yet the article does not detail how the gains translate to diverse workloads beyond the benchmark suite, leaving open the question of broader applicability. Moreover, while cost reductions are highlighted, the exact financial impact for typical research teams remains undefined.
In short, the NVFP4 recipe shows concrete performance improvements in a controlled setting, but further evidence will be needed to confirm its relevance across the full spectrum of AI training tasks.
Further Reading
- NVFP4 Trains with Precision of 16-Bit and Speed and Efficiency of 4-Bit - NVIDIA Developer Blog
- How Nvidia solved the accuracy tradeoff of training 4-bit LLMs - BD Tech Talks
- Accelerating large language models with NVFP4 quantization - Red Hat Developer
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Common Questions Answered
How does NVIDIA's NVFP4 format improve large language model training efficiency?
[nvidia.com](https://developer.nvidia.com/blog/nvfp4-trains-with-precision-of-16-bit-and-speed-and-efficiency-of-4-bit/) reveals that NVFP4 enables 4-bit pretraining by cutting memory needs and boosting arithmetic throughput. The format uses techniques like micro-block scaling, high-precision block encoding, and stochastic rounding to maintain model accuracy during large-scale training, allowing AI factories to scale more rapidly and sustainably.
What was the key achievement in NVIDIA's NVFP4 training research?
[tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/nvidia-details-efficiency-of-the-nvfp4-format-for-llm-training-new-paper-reveals-how-nvfp4-offers-benefits-over-fp8-and-bf16) reports that NVIDIA successfully trained a 12-billion-parameter model on 10 trillion tokens using NVFP4, which is the longest publicly documented training run in 4-bit precision. The experiment demonstrated that NVFP4 could achieve accuracy comparable to higher precision formats like FP8, showcasing its potential for efficient large-scale model training.
What specific techniques did NVIDIA use to enable stable 4-bit model training?
[huggingface.co](https://huggingface.co/papers/2509.25149) highlights that NVIDIA's approach included Random Hadamard transforms to bound block-level outliers, a two-dimensional quantization scheme for consistent representations, stochastic rounding for unbiased gradient estimation, and selective high-precision layers. These innovative techniques allowed the researchers to overcome traditional challenges of training in 4-bit precision, such as maintaining training stability and convergence.