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.

The context window is 16 characters. Talking about tokens per second is meaningless.

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.