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I’m new to neural nets, when should one use fp8 vs fp16 vs fp32?

Basically no one uses FP32 at inference time. BF16/FP16 is typically considered unquantized, whereas FP8 is lightly quantized. That being said there's pretty minimal quality loss at FP8 compared to 16-bit typically; Llama 3.1 405b, for example, only benchmarks around ~1% worse when run at FP8: https://blog.vllm.ai/2024/07/23/llama31.html

Every major inference provider other than Hyperbolic Labs runs Llama 3.1 405b at FP8, FWIW (e.g. Together, Fireworks, Lepton), so to compare against FP32 is misleading to say the least. Even Hyperbolic runs it at BF16.

Pretraining is typically done in FP32, although some labs (e.g. Character AI, RIP) apparently train in INT8: https://research.character.ai/optimizing-inference/

SambaNova does bf16

Higher the precision the better. Use what works within your memory constraints.

With serious diminishing returns. At inference time, no reason to use fp64 and should probably use fp8 or less. The accuracy loss is far less than you'd expect. AFAIK Llama 3.2 3B fp4 will outperform Llama 3.2 1B at fp32 in accuracy and speed, despite 8x precision.

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