Ollama vs Xinference
Short answer
Ollama: The simplest way to download and run open-weight LLMs locally behind an API. 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 Ollama if you want: larger community (185k vs 9.6k GitHub stars).
Choose Xinference if you want: simpler deploy (one container vs a multi-container stack).
Ollama 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
Ollama vs Xinference side by side
| Ollama | Xinference | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | The simplest way to download and run open-weight LLMs locally behind an API. | 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 | 185k | 9.6k |
| Last commit | Oct 26, 2026 | Oct 26, 2026 |
| Latest release | v0.40.11 | v4.1.0 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | 85 MiB idle, 1.9 GiB working (our test, v0.40.8, Oct 22, 2026) | Not tested yet |
| GPU | NVIDIA GPUs need compute capability 5.0+ and driver 550+ (570+ for compute capability 5.0–6.2). CPU-only also works. | 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 €19.99/mo (based on our test, not official) | Not estimated |
| Deploy method | Docker (or install script) | Docker |
| Known gotchas | 2 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Yes, by default | 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 Ollama guide → | Full Xinference guide → |
Key differences
- License: Ollama: MIT (Permissive). Xinference: Apache-2.0 (Permissive).
- Deploy: Ollama: Docker (or install script) · multi-container stack. Xinference: Docker · single container.
- GitHub stars: Ollama: 185k. Xinference: 9.6k.
- Latest release: Ollama: v0.40.11 (Oct 24, 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; Last commit: Oct 26, 2026.
Ollama vs Xinference: FAQ
Ollama or Xinference: which should a small business choose?
Choose Ollama if you want: larger community (185k vs 9.6k GitHub stars). Choose Xinference if you want: simpler deploy (one container vs a multi-container stack). Ollama and Xinference publish no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, Ollama or Xinference?
Ollama: 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 Ollama and Xinference need?
Ollama: not published. Xinference: not published.
What is the license difference between Ollama and Xinference?
Ollama: 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 Ollama or Xinference more actively maintained?
Ollama: Active, last commit Oct 26, 2026. Xinference: Active, last commit Oct 26, 2026. GitHub data refreshed Oct 26, 2026.
Which is easier to deploy, Ollama or Xinference?
Ollama installs via Docker (or install script) (multi-container stack); Xinference via Docker (single container). Ollama lists 2 known gotchas and Xinference lists 4; see each app page.
Related
- Ollama self-hosting guide · Xinference self-hosting guide
- All Ollama alternatives, ranked
- All Xinference alternatives, ranked
- GPUStack vs Ollama
- GPUStack vs Xinference
- Jan vs Ollama
- llama.cpp vs Ollama
- llama.cpp vs Xinference
- LocalAI vs Ollama
- LocalAI vs Xinference
- Ollama vs vLLM
- vLLM vs Xinference
- Add a third app in the interactive comparison
Sources
- Ollama GitHub repository: github.com/ollama/ollama
- Ollama documentation, Ollama deploy instructions: docs.ollama.com/docker
- Ollama license: github.com/ollama/ollama/blob/main/LICENSE
- Ollama hardware requirements: docs.ollama.com/gpu
- 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.