yeah, actually I wanted to see if this was possible at all. I managed to get around 3000 tokens/s on a ps2 with classic transformers, since the emotion engine is capable of 32 bit addresses, but it has like 32gb of ram. So I ran into the question of why was that fast and I couldn't get that speed even with small models, and the deal is that the instructions went right of the memory to the gpu and that's the main difference that does when a regular computer does inference: it has to request the instructions to the cpu every time. As I mentioned too, on professional cards you can avoid these problems naturally, since they got instructions precisely for this, but sadly I don't have 30k bucks to spare on a gpu :(
The $5/hr B200 rate is fine for training, but cloud latency usually breaks real-time signal processing. I’ve been hitting similar walls with MemeRadar; when you're processing high-volume spikes, the bottleneck is memory bandwidth, not raw TFLOPS. Quantizing to fit L3 cache is an option, but you lose the precision needed for spotting subtle rug-pull patterns. For 24/7 production workloads, local hardware TCO usually beats cloud rentals.
You can get lots of tokens per second on the CPU if the entire network fits in L1 cache. Unfortunately the sub 64 kiB model segment isn't looking so hot.
But actually ... 3000? Did GP misplace one or two zeros there?
I wondered the same, but the rendering seems right, the output was almost instant. I'll recheck the token counter; anyway as you say, fast isn't practical. Actually I had to develop my own tiny model https://huggingface.co/xaskasdf/brandon-tiny-10m-instruct to fit something "usable", and it's basically a liar or disinformation machine haha
