This research investigates how FP8 reduced-precision affects large language model training stability, helping organizations optimize computation without sacrificing model quality.

• FP8 precision can achieve comparable model quality to BF16 with proper stability considerations
• Researchers identified specific mathematical operations that cause instability in reduced-precision training
• The study provides practical guidelines for implementing FP8 training safely
• Potential for significant computational efficiency gains when deploying LLMs at scale

For engineering teams, this work offers a path to more cost-effective and energy-efficient LLM development while maintaining training stability—a critical advancement as model sizes continue to grow.

To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability