Easily Train Models with H100 GPUs on NVIDIA DGX Cloud

Update: This service is deprecated and no longer available as of April 10th, 2025.

Today, we are thrilled to announce the launch of Train on DGX Cloud, a new service on the Hugging Face Hub, available to Enterprise Hub organizations. Train on DGX Cloud makes it easy to use open models with the accelerated compute infrastructure of NVIDIA DGX Cloud. Together, we built Train on DGX Cloud so that Enterprise Hub users can easily access the latest NVIDIA H100 Tensor Core GPUs, to fine-tune popular Generative AI models like Llama, Mistral, and Stable Diffusion, in just a few clicks within the Hugging Face Hub.

GPU Poor No More

This new experience expands upon the strategic partnership we announced last year to simplify the training and deployment of open Generative AI models on NVIDIA accelerated computing. One of the main problems developers and organizations face is the scarcity of GPU availability, and the time-consuming work of writing, testing, and debugging training scripts for AI models. Train with DGX Cloud offers an easy solution to these challenges, providing instant access to NVIDIA GPUs, starting with H100 on NVIDIA DGX Cloud. In addition, Train with DGX Cloud offers a simple no-code training job creation experience powered by Hugging Face AutoTrain and Hugging Face Spaces.

Enterprise Hub organizations can give their teams instant access to powerful NVIDIA GPUs, only incurring charges per minute of compute instances used for their training jobs.

“Train on DGX Cloud is the easiest, fastest, most accessible way to train Generative AI models, combining instant access to powerful GPUs, pay-as-you-go, and no-code training,” says Abhishek Thakur, creator of Hugging Face AutoTrain. “It will be a game changer for data scientists everywhere!”

How it works

Training Hugging Face models on NVIDIA DGX Cloud has never been easier. Below you will find a step-by-step tutorial to fine-tune Mistral 7B.

You can monitor your training by opening the “logs” of the Space.