Pick llama.cpp if
engineers who are comfortable with a compiler or a release binary and a GGUF path.
Read the llama.cpp pagellama.cpp vs vLLM
Our take
Pick llama.cpp if engineers who are comfortable with a compiler or a release binary and a GGUF path. Pick vLLM if platform and inference engineers who already have a GPU (or a kube cluster) and need an OpenAI-shaped endpoint. Skip llama.cpp if someone who wants a downloadable chat window (Ollama, Jan, GPT4All) and will not pass -m model.gguf. Skip vLLM if someone who wants a local ChatGPT window (Ollama, Jan) and
Best for
llama.cpp Run an open model without a Python serving stack
vLLM Serving tokens at production QPS
Free plan
llama.cpp $0 · mIT. You pay disk, RAM, and (optionally) a GPU
vLLM $0 · apache-2.0. You pay the GPU, electricity, and (if you use them) model licenses
Lowest paid
llama.cpp Not published by the vendor
vLLM Not published by the vendor
Runs where
llama.cpp Local; you host it
vLLM Web, desktop; hosted
Watch out
llama.cpp You bring the weights
vLLM The GPU is the bill
Last checked
llama.cpp 2026-10-20 · github.com/ggml-org/llama.cpp
vLLM 2026-10-20 · github.com/vllm-project/vllm
Which one, then?
Pick vLLM if
platform and inference engineers who already have a GPU (or a kube cluster) and need an OpenAI-shaped endpoint.
Read the vLLM page