Great points, let me clarify:

- model size: 2B is just for this preview (it was faster to implement), our article explains how we expect to support large frontier MoE at 1,000 to 5,000 tokens/s

- reaching 500 tok/s, or even up to ~1,000 tok/s, on a consumer GPU card is possible with existing inference engines like vLLM. But there is a ceiling.

The hard part comes we you try to be faster than that: these frameworks won't scale higher just by adding GPUs or using faster GPUs. There is a "glass ceiling" due to microseconds lost everywhere in the stack (grid syncs, inter-GPU comms, kernel launches, CPU sampling, etc.).

All our work at Kog is about removing these bottlenecks.

That doesn't clarify anything lol. It's a bit click baity.