Mixed Precision

Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.

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Mixed Precision Training Best Practice

Use torch.cuda.amp for automatic mixed precision:

  • Wrap forward pass in torch.cuda.amp.autocast()
  • Use GradScaler for loss scaling
  • BF16 preferred over FP16 on Ampere+ GPUs (RTX 3xxx, A100, RTX 4xxx)
  • Watch for NaN gradients — reduce learning rate if needed
  • Do NOT use amp with custom CUDA kernels unless tested

thada2402/AutoResearchClaw/tree/main/researchclaw/skills/builtin/tooling/mixed-precision commit 3795517349

Frequently asked questions

npx skillmds@latest add thada2402/mixed-precision