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That post is uninteresting both because they miss the point, and it's not clear a human was even involved to perceive a point to miss. Sure, with an unlimited transistor budget, power budget, and a design clocked at 4GHz fabbed on 5nm one of the best CPU design teams in the world can make a thing that is straight line faster than a one-person project running at 80MHz on a 20 year old 65nm FPGA. Any other answer would be extremely surprising.

Now, there are a bunch of interesting things about this project. Seeing the example of a tiny transformer running on FPGA is informative, and that it was apparently a pretty quick project for one person + robot assistance. Probably some transferable lessons for anyone else doing robo-FPGA development.

https://github.com/fguzman82/gateGPT/tree/main/

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yeah, then theres prompt loading too.

but anyone who can fit QWEN-3.6 35B with a sustained ~30 token/s and ~100k context with cache could print money as a hardware vendor.

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with llama-cpp and offloading non-active experts (from MOE architecture) to cpu RAM, you can easily run 50 tok / s QWEN-3.6 35B on 8-12 GB of VRAM. KV cache is a few GB, experts are ~3-5 GB (assuming q8 quant from Unsloth for example).

You can scroll through r/localllama and find tons of people getting useable speeds out of Qwen 35B.

24 tok / second on an ancient 1080ti

https://old.reddit.com/r/LocalLLaMA/comments/1tcc7h5/24_toks...

100 tok / second on a 4070

https://old.reddit.com/r/LocalLLaMA/comments/1tjh7az/110_tok...

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That just sounds like a 3090.
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not at the vram sizes that control how much context to load; also, GPUs arn't as effiecient as direct inference.
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