llama.cpp vs vLLM
Two of the top model serving, side by side: score, setup, license, activity and what each review found.
llama.cpp
C/C++ inference engine serving GGUF models over an OpenAI-compatible API
vLLM
High-throughput LLM serving engine with OpenAI and Anthropic APIs
| What we compare | llama.cpp | vLLM |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 94, widely used | 91, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 81, healthy |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 130.8k | 93.6k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Mentioned |
llama.cpp
llama.cpp is a C/C++ inference engine for LLMs and VLMs with no dependencies, built on ggml. llama serve starts an OpenAI-compatible API server with a built-in web UI, pulling GGUF models straight from Hugging Face, with 1.5 to 8-bit quantization and CPU+GPU hybrid offload for models larger than VRAM. Backends cover CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, CANN, MUSA and WebGPU.
Who it is for: engineers who want a lean local inference server
Strengths
- Plain C/C++ with no runtime dependencies; prebuilt binaries and Docker
- Backends for NVIDIA, AMD, Apple Metal, Intel SYCL, Vulkan, Ascend, Moore Threads
- Hybrid CPU+GPU offload runs models larger than available VRAM
- Built-in web UI and OpenAI-compatible server via llama serve
Weaknesses
- GGUF model format only
- README gives no port, auth or sizing guidance; see tools/server docs
- Install script is curl piped to sh; otherwise build from source
- OpenVINO backend still in progress
- GPU optional
- Docker
- Models: GGUF models from Hugging Face (e.g. Qwen3.5-0.8B-GGUF)
vLLM
vLLM is a Python serving engine for Hugging Face models that batches requests continuously with PagedAttention, prefix caching and speculative decoding, exposing an OpenAI-compatible API plus Anthropic Messages API and gRPC. It covers 200+ architectures (dense, MoE, multimodal, embedding) with FP8, INT8, GPTQ, AWQ and GGUF quantization and tensor, pipeline and expert parallelism.
Who it is for: teams serving open models to many concurrent users
Strengths
- Continuous batching with PagedAttention for high multi-user throughput
- 200+ Hugging Face architectures including MoE, multimodal and embedding models
- OpenAI, Anthropic Messages and gRPC endpoints with tool calling and structured output
- Runs on NVIDIA, AMD, Intel GPUs, CPUs, TPUs, Gaudi, Ascend via plugins
Weaknesses
- No web UI; API server only
- README gives no VRAM, port or model-size guidance
- Heavy Python, PyTorch and CUDA dependency chain; no single binary
- Most optimized kernels target NVIDIA and AMD GPUs; CPU path is secondary
- GPU optional
- Docker
- Models: 200+ Hugging Face architectures: Llama, Qwen, Gemma, Mixtral, DeepSeek-V3, GPT-OSS, LLaVA, Qwen-VL, E5-Mistral