mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-07 20:47:30 +02:00
llama-bench differs from llama-server in 2 ways: 1. It attaches a persistent threadpool across cgraph invocations 2. It has a polling scheme of 50 (as opposed to 0 of args) Together, this causes a significant perf regression in the CUDA backend on Windows when the CPU backend is built with OpenMP disabled. Why is the CPU backend affecting the CUDA backend? Typically, we have a LUT on the CPU backend where we map Token_ID -> Embedding to save on VRAM budget.