Head to head · Coding · updated Oct 25, 2026
llama.cpp vs vLLM
llama.cpp scores 93 to vLLM's 92 on public evidence for run LLMs locally, and they rank #2 and #4 there. Both are free to start. Numbers below are the same public signals the ranking uses, recomputed nightly — nothing here is sponsored.
llama.cpp
C/C++ inference engine for LLMs that runs quantized GGUF models on CPUs and GPUs.
+ The engine underneath Ollama, LM Studio, and most local tooling, with the widest hardware support.
− Building and tuning flags yourself is a chore compared with wrappers.
vLLM
High-throughput LLM serving engine with PagedAttention and an OpenAI-compatible server.
+ Serves many concurrent requests on GPUs far faster than llama.cpp-based tools.
− Linux and NVIDIA-centric; painful on Mac or consumer hardware.
Side by side
public evidence| llama.cpp | vLLM | |
|---|---|---|
| Evidence score / 100 | 93 | 92 |
| GitHub stars | 132,268 | 95,387 |
| Hacker News stories, 30d | 27 | 0 |
| Release-feed mentions, 30d | 68 | 26 |
| Last activity | 0 days ago | 0 days ago |
| Pricing | Open source · Free | Open source · Free |
| Platforms | mac, windows, linux, cli, api | linux, cli, api |
| Maker | ggml (Georgi Gerganov) | vLLM Project |
Which one?
Pick llama.cpp if
- you want the stronger public evidence (93 vs 92)
- you need it on mac, windows
Best for developers who want maximum control over local inference.
Pick vLLM if
Serves many concurrent requests on GPUs far faster than llama.cpp-based tools.
Best for self-hosting models for production traffic.