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head to head · open source

llama.cpp vs vllm

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. vllm has 92,028 stars, 22,335 forks, 7,953 open issues and last shipped yesterday. llama.cpp leads on adoption by 40% (128,581 vs 92,028 stars). llama.cpp is written in C++ under MIT; vllm is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, vllm 24%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama.cpp ★ 129K vllm ★ 92K category AI & Machine Learning

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Side by side

llama.cpp vllm
GitHub stars ★ 129K ★ 92K
License MIT Apache-2.0
Written in C++ Python
Last push 2026-09-17 2026-09-17
Forks ⑂ 23K ⑂ 22K
Self-hosting Yes Yes
Data ownership Your server Your server

pick llama.cpp if

  • You weight community size — 129K stars and counting
  • You want the MIT license terms
  • Your stack matches C++
  • You value the larger contributor base for long-term maintenance

full llama.cpp profile →

pick vllm if

  • You want the vllm feature set and don't need the biggest community
  • You prefer the Apache-2.0 license terms
  • Your stack matches Python
  • You evaluated both and vllm fits your workflow better

full vllm profile →

About llama.cpp

llama.cpp is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

read the full llama.cpp overview →

About vllm

vLLM is a high throughput, memory efficient Python library for LLM inference and serving, built for ML engineers and platform teams who need to run open weight models on their own hardware at production scale.

read the full vllm overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 246K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai ollama vs pageindex ollama vs langfuse openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat dify vs langchain dify vs ponytail langchain vs ponytail dify vs graphify langchain vs graphify ponytail vs graphify dify vs claude-mem dify vs ragflow strix vs shannon

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama.cpp vs Ollama llama.cpp vs GPT4All llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec llama.cpp vs langchain4j llama.cpp vs txtai

Frequently asked questions

Is llama.cpp or vllm more popular?

llama.cpp has 128,581 GitHub stars and vllm has 92,028. llama.cpp has the larger community by that measure.

Are llama.cpp and vllm free?

Both are open source. llama.cpp is licensed under MIT and vllm under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and vllm?

llama.cpp is written in C++ and vllm in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama.cpp or vllm?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose vllm if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.