Maximizing Performance in Large-Scale Distributed GPU Environments: Practical Performance Engineering with NVIDIA H100 (64 GPUs)

Overview

This white paper details a joint performance engineering initiative between Fixstars and SAKURA internet, utilizing the "Koukaryoku PHY" GPU cloud service. Using a cluster of 64 NVIDIA H100 GPUs (8 nodes × 8 GPUs), we demonstrate how systematic performance engineering can dramatically improve the cost-efficiency of both large-scale AI training and massive-model inference. By optimizing parameters such as batch sizes and 3D parallelism, and automating environment setup with Fixstars AIBooster, we achieved up to 3x the cost-performance compared to standard API services.

What You'll Learn
  • Techniques for 8-Node Optimization: How to navigate the trade-offs between computational power and communication overhead in multi-node configurations.
  • Continuous Pre-training at Scale: Strategies to achieve 2x cost-efficiency through global batch size optimization and automated hyperparameter tuning.
  • Massive Model Inference: Practical results of running a 480B parameter model with 1M token context, achieving 3x better cost-performance than existing inference APIs.
  • Automated Environment Setup: How to leverage Ansible and Fixstars AIBooster to automate the construction of optimized AI processing environments in approximately 2 hours.
Recommended For
  • AI/ML Engineers looking to maximize the throughput of large-scale distributed training on H100 clusters.
  • Infrastructure Architects interested in automating and optimizing high-performance computing (HPC) environments for AI.
  • Technical Executives aiming to reduce the operational costs of LLM pre-training and massive-scale inference.
  • Product Managers evaluating the cost-benefit of private GPU clouds versus public LLM API services.
Download the White Paper