Will AI accelerators improve in FLOPs/watt by 100x of an NVidia H100 by 2033?
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Compared to an H100, will tensor TFLOPs/Watt improve by 100x by 2033? AI accelerators in scope for this question must be deployed significantly - with at least 100k units or $100M (in 2024 dollars) in production, and have published perf/watt numbers.

This market will count peak FLOPs/watt at k bits of precision, adjusted by a factor of 2^(1 - 32/k). That is, 16 bit precision counts 1/4 as much as 32 bit, which counts 1/4 as much as 64 bit precision.

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Two questions:

  • What's the TFLOPs/Watt of an H100 today?

  • What floating point precision is this about? Double-precision?

GPUs usually don't do higher than single precision (32 bit), It's considered unnecessary for graphics or neural networs. But good question, modern ai systems do often use even lower precision.

Well H100 supports double precision 🤷‍♂️

In practice modern AI is usually much less than 32 bit; 16 bit is the large end. Honestly I'd recommend counting any of 8, 16, or 32 bit flop/s/w performance for this question.

This market will use an adjusted flop/s/w by adjusting k-bit precision by a factor of 2^(1 - 32/k). That is, 16 bit precision counts 1/4 as much as 32 bit, which counts 1/4 as much as 64 bit precision.

Whichever precision gets the highest score is counted as the FLOP/s/W for the chip.

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