Acing the Test: NVIDIA Turbocharges Generative AI Training in MLPerf Benchmarks
Summary
The benchmark uses a portion of the full GPT-3 data set behind the popular ChatGPT service that, by extrapolation, Eos could now train in just eight days, 73x faster than a prior state-of-the-art system using 512 A100 GPUs. It’s heavy lifting that makes large language models widely available so every business can adopt them with tools like NVIDIA NeMo, a framework for customizing LLMs. In a new generative AI test this round, 1,024 NVIDIA Hopper architecture GPUs completed a training benchmark based on the Stable Diffusion text-to-image model in 2.5 minutes, setting a high bar on this new workload. Speedups translate to faster time to market, lower costs and energy savings for users training massive LLMs or customizing them with frameworks like NeMo for the specific needs of their business. Eleven systems makers used the NVIDIA AI platform in their submissions this round, including ASUS, Dell Technologies, Fujitsu, GIGABYTE, Lenovo, QCT and Supermicro.