Accelerating Large Language Models with Mixed-Precision Techniques

Mixed-precision training combines FP16 or bfloat16 computations with FP32 weight updates to reduce GPU memory use and accelerate deep-learning training while preserving numerical stability, unlike full FP16 training, which can suffer from overflow, underflow, and failed convergence. The discussion explains how floating-point formats trade precision and numerical range for efficiency, with FP32 serving as the usual deep-learning default and bfloat16 offering a wider dynamic range than conventional FP16. In a DistilBERT sentiment-classification benchmark on an A100 GPU, changing a PyTorch Lightning Fabric setting to FP16 mixed precision reduced runtime from 21.75 to 7.25 minutes and memory use from 5.37 to 4.31 GB, while test accuracy increased from 89.92% to 92.15%; full FP16 training was faster and smaller but failed to converge. Tensor Core settings that lower matrix-multiplication precision can similarly improve FP32 performance, and bfloat16 mixed precision produced comparable results with slightly higher accuracy in the reported experiment. For LLaMA inference, bfloat16 approximately halved memory consumption and increased generation speed by about 30% relative to FP32, while further gains may be available through low-bit integer quantization.

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