llama.cpp vs Xinference
Short answer
llama.cpp: Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. Xinference: Model-serving platform for LLM, embedding, rerank, speech and image models behind one OpenAI-compatible API. Both are in our Local model runtimes category.
Choose llama.cpp if you want: larger community (132k vs 9.6k GitHub stars).
Choose Xinference if you want: simpler deploy (one container vs a multi-container stack).
llama.cpp and Xinference publish 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 Xinference side by side
| llama.cpp | Xinference | |
|---|---|---|
| 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. | Model-serving platform for LLM, embedding, rerank, speech and image models behind one 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; keep the Apache-2.0 license and notices. Xinference Enterprise is a separate commercial edition. Models keep their own licenses. |
| Status | Active | Active |
| GitHub stars | 132k | 9.6k |
| Last commit | Oct 25, 2026 | Oct 26, 2026 |
| Latest release | v0.7.0 | v4.1.0 |
| 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. | The default Docker image requires an NVIDIA GPU (host CUDA 13.0 or newer, driver 580 or newer, NVIDIA Container Toolkit). For CPU, use the image tag with a -cpu suffix; pip installs on Apple Silicon can use the MLX engine. |
| Est. server cost | from €11.99/mo (based on our test, not official) | Not estimated |
| Deploy method | Docker (or install script / prebuilt binaries) | Docker |
| Known gotchas | 3 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Not documented | Yes, by default |
| SSO (OIDC / SAML / LDAP) | Not documented | Free (OIDC free) |
| Audit log | Not documented | Yes, free |
| RBAC | Not documented | Yes, free |
| Details | Full llama.cpp guide → | Full Xinference guide → |
Key differences
- License: llama.cpp: MIT (Permissive). Xinference: Apache-2.0 (Permissive).
- Deploy: llama.cpp: Docker (or install script / prebuilt binaries) · multi-container stack. Xinference: Docker · single container.
- GitHub stars: llama.cpp: 132k. Xinference: 9.6k.
- Last commit: llama.cpp: Oct 25, 2026. Xinference: Oct 26, 2026.
- Latest release: llama.cpp: v0.7.0 (Oct 15, 2026). Xinference: v4.1.0 (Oct 25, 2026).
Same for both: Category: Local model runtimes; Official hardware: not published; GPU: optional / see notes; Est. server cost: not estimated (no official RAM figure); Status: Active.
llama.cpp vs Xinference: FAQ
llama.cpp or Xinference: which should a small business choose?
Choose llama.cpp if you want: larger community (132k vs 9.6k GitHub stars). Choose Xinference if you want: simpler deploy (one container vs a multi-container stack). llama.cpp and Xinference publish no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, llama.cpp or Xinference?
llama.cpp: not estimated (no official RAM figure). Xinference: not estimated (no official RAM figure). 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 Xinference need?
llama.cpp: not published. Xinference: not published.
What is the license difference between llama.cpp and Xinference?
llama.cpp: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. Xinference: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. Xinference Enterprise is a separate commercial edition. Models keep their own licenses. This is a summary, not legal advice.
Is llama.cpp or Xinference more actively maintained?
llama.cpp: Active, last commit Oct 25, 2026. Xinference: Active, last commit Oct 26, 2026. GitHub data refreshed Oct 26, 2026.
Which is easier to deploy, llama.cpp or Xinference?
llama.cpp installs via Docker (or install script / prebuilt binaries) (multi-container stack); Xinference via Docker (single container). llama.cpp lists 3 known gotchas and Xinference lists 4; see each app page.
Related
- llama.cpp self-hosting guide · Xinference self-hosting guide
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- All Xinference alternatives, ranked
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- GPUStack vs Xinference
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- llama.cpp vs LocalAI
- llama.cpp vs Ollama
- llama.cpp vs vLLM
- LocalAI vs Xinference
- Ollama vs Xinference
- vLLM vs Xinference
- Add a third app in the interactive comparison
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
- Xinference GitHub repository: github.com/xorbitsai/inference
- Xinference documentation, Xinference hardware requirements, Xinference deploy instructions: inference.readthedocs.io/en/latest/getting_started/using_docker_image.html
- Xinference license: github.com/xorbitsai/inference/blob/main/LICENSE
- Hetzner Cloud pricing used for cost estimates (read 2026-10-26): 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.