[RandyOrion]

This project shows an interesting automated search for engineering problems that I like to see more.

The experience of utilizing tiered storage (gpu vram, ram, and ssd) is generally poor for a lot of LLM inference engines out there, e.g., llama.cpp, sglang, vllm, etc..

My own experience shows that both weight and KV cache offload to ram on sglang and vllm is unavailable or unusable. Copying extra parameters from documents and adding them to already working commands results in errors. Llama.cpp does support weight offload, but the experience is not pleasant, low pcie (gpu <-> ram) utilization, low gpu utilization, and really low tokens per second.