GPUStack vs Xinference
Short answer
GPUStack: GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible 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 Xinference if you want: no GPU server needed and larger community (9.6k vs 5.9k GitHub stars).
Xinference 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
GPUStack vs Xinference side by side
| GPUStack | Xinference | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible API. | Model-serving platform for LLM, embedding, rerank, speech and image models behind one OpenAI-compatible API. |
| License | Apache-2.0 | Apache-2.0 |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. GPUStack Enterprise is a separate commercial edition. Models keep 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 | 5.9k | 9.6k |
| Last commit | Oct 26, 2026 | Oct 26, 2026 |
| Latest release | v2.3.0 | v4.1.0 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | Not tested yet | Not tested yet |
| GPU | Required on worker nodes (Linux only): for NVIDIA, a driver supporting CUDA 12.8 or higher plus the NVIDIA Container Toolkit; for AMD, ROCm 7.0 or higher. The GPUStack server itself does not need a GPU. | 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 | GPU server (not estimated) | Not estimated |
| Deploy method | Docker | Docker |
| Known gotchas | 4 listed | 4 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Yes, by default | Yes, by default |
| SSO (OIDC / SAML / LDAP) | Free (OIDC free · SAML free) | Free (OIDC free) |
| Audit log | Not documented | Yes, free |
| RBAC | Basic free, more in paid plan | Yes, free |
| Details | Full GPUStack guide → | Full Xinference guide → |
Key differences
- GPU: GPUStack: needed for practical use. Xinference: optional / see notes.
- Est. server cost: GPUStack: GPU server (not estimated). Xinference: not estimated (no official RAM figure).
- GitHub stars: GPUStack: 5.9k. Xinference: 9.6k.
- Latest release: GPUStack: v2.3.0 (Oct 23, 2026). Xinference: v4.1.0 (Oct 25, 2026).
Same for both: Category: Local model runtimes; License: Apache-2.0 (Permissive); Official hardware: not published; Deploy: Docker · single container; Status: Active; Last commit: Oct 26, 2026.
GPUStack vs Xinference: FAQ
GPUStack or Xinference: which should a small business choose?
Choose Xinference if you want: no GPU server needed and larger community (9.6k vs 5.9k GitHub stars). Xinference publishes no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, GPUStack or Xinference?
GPUStack: GPU server (not estimated). 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 GPUStack and Xinference need?
GPUStack: not published. Xinference: not published. GPUStack needs a GPU for practical use.
What is the license difference between GPUStack and Xinference?
GPUStack: Apache-2.0 (Permissive). Permissive. Internal and commercial use allowed; keep the Apache-2.0 license and notices. GPUStack Enterprise is a separate commercial edition. Models keep 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 GPUStack or Xinference more actively maintained?
GPUStack: Active, last commit Oct 26, 2026. Xinference: Active, last commit Oct 26, 2026. GitHub data refreshed Oct 26, 2026.
Which is easier to deploy, GPUStack or Xinference?
GPUStack installs via Docker (single container); Xinference via Docker (single container). GPUStack lists 4 known gotchas and Xinference lists 4; see each app page.
Related
- GPUStack self-hosting guide · Xinference self-hosting guide
- All GPUStack alternatives, ranked
- All Xinference alternatives, ranked
- GPUStack vs llama.cpp
- GPUStack vs LocalAI
- GPUStack vs Ollama
- GPUStack vs vLLM
- llama.cpp vs Xinference
- LocalAI vs Xinference
- Ollama vs Xinference
- vLLM vs Xinference
- Add a third app in the interactive comparison
Sources
- GPUStack GitHub repository: github.com/gpustack/gpustack
- GPUStack documentation, GPUStack hardware requirements: docs.gpustack.ai/latest/installation/requirements/
- GPUStack license: github.com/gpustack/gpustack/blob/main/LICENSE
- GPUStack deploy instructions: github.com/gpustack/gpustack#readme
- 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.