Training LLMs on H100 PCIe GPUs in the Cloud: Setup and Optimization

As large language models (LLMs) grow in complexity, the demand for high-performance GPUs like NVIDIA's H100, built on the Hopper architecture, has increased, with the H100 PCIe variant offering a cost-effective and scalable option, especially in cloud environments. This guide provides a comprehensive walkthrough on setting up a training environment using H100 PCIe GPUs on Runpod, utilizing frameworks such as DeepSpeed and Fully Sharded Data Parallel (FSDP) to optimize performance. Despite having slightly lower bandwidth compared to their SXM counterparts, H100 PCIe GPUs retain essential innovations like Transformer Engine support, NVLink, and FP8 precision, making them compatible with popular AI frameworks and advantageous for LLM training. The guide emphasizes the benefits of using these GPUs in cloud-based setups, highlighting their accessibility, reduced upfront costs, and high availability, and includes tips on optimizing data parallelism, checkpointing, and storage to fully exploit the capabilities of H100 PCIe GPUs.