Bfloat16 for LLM Training

This post details the benefits of using the bfloat16 data format for training large language models (LLMs) on Cerebras hardware. It explains how bfloat16, with its wider dynamic range compared to float16, improves training throughput by 18%, reduces weight growth, and leads to better evaluation scores. The post also covers how to enable bfloat16 in mixed precision mode, noting that it often doesn't require loss scaling, making it a near drop-in replacement for float32. This capability enhances the efficiency and stability of LLM training.