[SuchAnonMuchWow]

Its worse than that: the energy gains are when comparing computations made with fp32, but for fp8 the multipliers are really tiny and the adder/shifters represent a largest part of the operators (energy-wise and area-wise) and this paper will only have small gains.

On fp8, the estimated gate count of fp8 multipliers is 296 vs. 157 with their technique, so the power gain on the multipliers will be much lower (50% would be a more reasonable estimation), but again for fp8 the additions in the dot products are a large part of the operations.

Overall, its really disingenuous to claim 80% power gain and small drop in accuracy, when the power gain is only for fp32 operations and the small drop in accuracy is only for fp8 operators. They don't analyze the accuracy drop in fp32, and don't present the power saved for fp8 dot product.

[bobsyourbuncle]

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

[reissbaker]

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/

[ericlewis]

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