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How DeepSeek Optimized Training — FP8 Framework
The emergence of Large Language Models (LLMs) has transformed the AI landscape, driving advancements across industries. However, training these massive models demands immense computational resources, which has spurred interest in optimization techniques, such as reducing precision during training. While most LLMs today rely on float16 (FP16) precision for a balance of computational efficiency and model accuracy, there is growing interest in pushing precision further down to float 8-bit (FP8).
Reducing the precision to FP8 offers a promising route to cut costs and enhance training speed, but it comes with significant challenges. This blog dives into the current state of precision in LLM training, the potential benefits of FP8, and the technical hurdles associated with adopting it.
Why Float16 is Popular
Most LLMs, such as GPT, BERT, and their derivatives, are trained using float16 (FP16) or bfloat16 (BF16) precision. These formats strike a balance between computational efficiency and maintaining sufficient numerical accuracy to ensure convergence and model performance.
Advantages of FP16:
- Reduced Memory Usage: FP16 halves the memory requirements compared to float32, enabling larger batch sizes and models to fit in GPU memory.
