6/16/2026 at 4:12:26 PM
by laxmena
6/16/2026 at 4:57:30 PM
The context window is 16 characters. Talking about tokens per second is meaningless.by genxy
6/16/2026 at 5:30:35 PM
its not meaningless. there could be usecases like spell correction.by dominotw
6/16/2026 at 6:06:47 PM
It is only interesting as an academic exercise in EDA design. Just like microGPT. For something with an n^2 complexity and advertising perf is clickbait.by genxy
6/16/2026 at 5:18:14 PM
Transformers scale poorly vs. context window size and parameter count.Which means really impressive when those N’s are small!
I’m but a pundit in this area so don’t know much. But one wonders if there’s a future in burning larger models to FPGAs - whether big enough FPGAs exist (or can be built), and whether locating specialized compute right with the memory it needs can speed things up.
Likely would need a lot of algorithm parallelism work that’d translate back to CPUs/GPUs.
by cadamsdotcom
6/17/2026 at 12:59:33 AM
Huge FPGAs don’t really exist, but you can couple many together with high-speed interconnects.They will never be as fast as an NPU designed to run large models, though. GPUs are extremely general purpose in comparison, and FPGAs are about as general purpose as one can get.
by naikrovek
6/16/2026 at 5:54:53 PM
Related: https://www.spheron.network/blog/etched-ai-sohu-vs-nvidia-tr...by T-A
6/16/2026 at 4:27:37 PM
See also:https://rits.shanghai.nyu.edu/ai/karpathys-microgpt-on-fpga-...
TL;DR: The CPU implementation was 71x faster than the FPGA.
Note: model has only 4192 parameters.
by amelius
6/16/2026 at 5:24:10 PM
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.
by hedgehog
6/16/2026 at 4:58:22 PM
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.
by cyanydeez
6/16/2026 at 7:21:36 PM
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...
by upboundspiral
6/16/2026 at 5:13:51 PM
That just sounds like a 3090.by wmf
6/16/2026 at 7:01:38 PM
not at the vram sizes that control how much context to load; also, GPUs arn't as effiecient as direct inference.by cyanydeez
6/16/2026 at 9:15:01 PM
OK, B70.by wmf
6/20/2026 at 1:22:11 AM
[dead]by wellnessb
6/20/2026 at 1:22:37 AM
[dead]by wellnessb