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Piggybacking as you may find it useful. I built https://flopper.io and aim to do this differently that OP. We started with datasheets and individual GPUs. We're trying to get as many as we can.

Currently trying to work to have a plugin of similar to post back to get accurate benchmarks.

Take a look if you're interested!



I took a tiny glance. At least you're taking into account compute and memory bandwidth.

You need to get more desktop GPUs on here, for the inference at home people.


Yes, we're trying to get those ingested. We have quite a few but you raise a good point that inference at home is rising!

Part of the problem we have is some of those desktop GPUs don't have power and flops data. We're toying with the idea of writing a CLI to run/publish these but obviously a lot of work.

Cheers for engaging!


https://en.wikipedia.org/wiki/List_of_AMD_graphics_processin...

https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...

If you know what any given model uses for BW per token and flops per token, you can roughly infer for many models until you get better benchmarks.

There is also:

CUDA: https://github.com/ggml-org/llama.cpp/discussions/15013

HIP: https://github.com/ggml-org/llama.cpp/discussions/15021

Vulkan: https://github.com/ggml-org/llama.cpp/discussions/10879

Metal: https://github.com/ggml-org/llama.cpp/discussions/4167

These three are measured against the old model of Llama 2 7B, but it also means the numbers are without any per-model quirks that reduce compute and memory BW usage, thus more or less generically applicable.


When I try to search for something I get an endless cloudflare captcha loop.


Interesssting! Thanks for reporting stranger ranger danger!

Will take a look




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