llama.cpp vs vLLM
Short answer
llama.cpp: Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. vLLM: High-throughput LLM inference server for GPUs with an OpenAI-compatible API. Both are in our Local model runtimes category.
Choose llama.cpp if you want: no GPU server needed.
llama.cpp publishes no official RAM figure, so we cannot compare hosting cost directly.
Last updated · computed from each project's official docs and live GitHub data · sources
llama.cpp vs vLLM side by side
| llama.cpp | vLLM | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. | High-throughput LLM inference server for GPUs with an OpenAI-compatible API. |
| License | MIT | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed. Individual models have their own licenses. | Permissive. Internal and commercial use allowed. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 132k | 95.1k |
| Last commit | Oct 22, 2026 | Oct 22, 2026 |
| Latest release | v0.7.0 | v0.32.0 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | 437 MiB idle, 489 MiB working (our test, vbuild 11804 (c7fc50941), Oct 22, 2026) | Not tested yet |
| GPU | Optional. Runs on CPU (AVX/AVX2/AVX512/AMX, ARM NEON) and supports CPU+GPU hybrid inference for models larger than VRAM. Docker images exist for CUDA, ROCm, SYCL, Vulkan, MUSA and OpenVINO. | NVIDIA: compute capability 7.5 or higher (e.g. T4, RTX 20xx, A100, L4, H100, B200). |
| Est. server cost | from €11.99/mo (based on our test, not official) | GPU server (not estimated) |
| Deploy method | Docker (or install script / prebuilt binaries) | pip / uv (or Docker) |
| Known gotchas | 3 listed | 1 listed |
| Telemetry | Not documented | On by default |
| Calls home by default | Not documented | Not documented |
| SSO (OIDC / SAML / LDAP) | Not documented | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Not documented | Not documented |
| Details | Full llama.cpp guide → | Full vLLM guide → |
Key differences
- License: llama.cpp: MIT (Permissive). vLLM: Apache-2.0 (Permissive).
- GPU: llama.cpp: optional / see notes. vLLM: needed for practical use.
- Est. server cost: llama.cpp: not estimated (no official RAM figure). vLLM: GPU server (not estimated).
- Deploy: llama.cpp: Docker (or install script / prebuilt binaries) · multi-container stack. vLLM: pip / uv (or Docker) · pip / uv (or Docker).
- GitHub stars: llama.cpp: 132k. vLLM: 95.1k.
- Latest release: llama.cpp: v0.7.0 (Oct 15, 2026). vLLM: v0.32.0 (Oct 18, 2026).
Same for both: Category: Local model runtimes; Official hardware: not published; Status: Active; Last commit: Oct 22, 2026.
llama.cpp vs vLLM: FAQ
llama.cpp or vLLM: which should a small business choose?
Choose llama.cpp if you want: no GPU server needed. llama.cpp publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, llama.cpp or vLLM?
llama.cpp: not estimated (no official RAM figure). vLLM: GPU server (not estimated). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.
How much hardware do llama.cpp and vLLM need?
llama.cpp: not published. vLLM: not published. vLLM needs a GPU for practical use.
What is the license difference between llama.cpp and vLLM?
llama.cpp: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. vLLM: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed. Models keep their own licenses. This is a summary, not legal advice.
Is llama.cpp or vLLM more actively maintained?
llama.cpp: Active, last commit Oct 22, 2026. vLLM: Active, last commit Oct 22, 2026. GitHub data refreshed Oct 22, 2026.
Which is easier to deploy, llama.cpp or vLLM?
llama.cpp installs via Docker (or install script / prebuilt binaries) (multi-container stack); vLLM via pip / uv (or Docker) (pip / uv (or Docker)). llama.cpp lists 3 known gotchas and vLLM lists 1; see each app page.
Related
Sources
- llama.cpp GitHub repository: github.com/ggml-org/llama.cpp
- llama.cpp documentation: github.com/ggml-org/llama.cpp/blob/master/docs/docker.md
- llama.cpp license: github.com/ggml-org/llama.cpp/blob/master/LICENSE
- llama.cpp hardware requirements: github.com/ggml-org/llama.cpp#description
- llama.cpp deploy instructions: github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md#docker
- vLLM GitHub repository: github.com/vllm-project/vllm
- vLLM documentation, vLLM hardware requirements: docs.vllm.ai/en/latest/getting_started/installation/gpu/
- vLLM license: github.com/vllm-project/vllm/blob/main/LICENSE
- vLLM deploy instructions: github.com/vllm-project/vllm#readme
- Hetzner Cloud pricing used for cost estimates (read 2026-10-22): www.hetzner.com/cloud/regular-performance
- GitHub REST API (stars, commits, releases): docs.github.com/en/rest
Every value is generated from the same verified data as the app pages. Spotted something wrong? Tell us.