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 SGLang
Our take
Pick llama.cpp if engineers who are comfortable with a compiler or a release binary and a GGUF path. Pick SGLang if inference engineers who already have GPUs and will run sglang serve (or the Cookbook one-click recipe), not a team looking for a $20 chat plan. Skip llama.cpp if someone who wants a downloadable chat window (Ollama, Jan, GPT4All) and will not pass -m model.gguf.
llama.cpp
C/C++ inference engine for GGUF models
Best for
llama.cpp Run an open model without a Python serving stack
SGLang Serve tokens (or diffusion frames) on GPUs you already own
Free plan
llama.cpp $0 · mIT. You pay disk, RAM, and (optionally) a GPU
SGLang $0
Lowest paid
llama.cpp Not published by the vendor
SGLang Not published by the vendor
Runs where
llama.cpp Local; you host it
SGLang Hosted
Watch out
llama.cpp You bring the weights
SGLang You bring the GPU
Last checked
llama.cpp 2026-10-20 · github.com/ggml-org/llama.cpp
SGLang 2026-10-22 · github.com/sgl-project/sglang/blob
Which one, then?
Pick SGLang if
inference engineers who already have GPUs and will run sglang serve (or the Cookbook one-click recipe), not a team looking for a $20 chat plan.
Read the SGLang page