How to Set Up a Multi-GPU Linux Machine for Deep Learning in 2024
DEEP LEARNING WITH MULTIPLE GPUS
As Deep Learning models (especially LLMs) keep getting bigger, the need for more GPU memory (VRAM) is ever-increasing for developing them and using them locally. Building or obtaining a multi-GPU machine is just the first part of the challenge. Most libraries and applications only use a single GPU by default. Thus, the machine also needs to have appropriate drivers along with libraries that can leverage the multi-GPU setup.
This story provides a guide on how to set up a multi-GPU (Nvidia) Linux machine with important libraries. This will hopefully save you some time on experimentation and get you started on your development.
At the end, links are provided to popular open-source libraries that can leverage the multi-GPU setup for Deep Learning.
Target
Set up a Multi-GPU Linux system with necessary libraries such as CUDA Toolkit and PyTorch to get started with Deep Learning
