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Decision brief
Unified production-ready inference API that supports a wide range of models and deployment methods.
Good fit when
- - When you need to deploy multiple types of models (like speech, text, and multimodal) through a single unified interface.
- - If your project requires flexibility in model switching with minimal code changes, allowing for testing different models quickly.
Avoid when
- - When strict control over individual model interfaces is required and a unified API complicates your workflow.
- - If you’re working with proprietary models that aren’t supported by Xinference’s built-in or custom integration mechanisms.
- Pricing:
- freemium - Primary core services offer under free Apache-2.0 license; advanced support might incur costs based on the deployment scale and environment complexity.
- Requirements:
- Min 4 GB RAM; Requires Docker; Compatibility with Nvidia GPUs requires Docker, CUDA setup.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install inferenceSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Xinference allows running multiple open-source, speech, and multimodal models through a single inference API, supporting cloud, on-premises, and laptop deployments.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 2, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 2, 2026)
via the command line, or via the Xinference’s python client. Check out our [docs]( https://inference.readthedocs.io/en/latest/gettingSource link
Tags
README
Getting Started
Docker
Nvidia GPU users can start Xinference server using Xinference Docker Image. Prior to executing the installation command, ensure that both Docker and CUDA are set up on your system.
docker run --name xinference -d -p 9997:9997 -e XINFERENCE_HOME=/data -v </on/your/host>:/data --gpus all xprobe/xinference:latest xinference-local -H 0.0.0.0
install xinference
helm install xinference xinference/xinference -n xinference --version 0.0.1-v<xinference_release_version>
For more customized installation methods on K8s, please refer to the [documentation](https://inference.readthedocs.io/en/latest/getting_started/using_kubernetes.html).
---
### Quick Start
Install Xinference by using pip as follows. (For more options, see [Installation page](https://inference.readthedocs.io/en/latest/getting_started/installation.html).)
```bash
pip install "xinference[all]"
To start a local instance of Xinference, run the following command:
$ xinference-local
Once Xinference is running, there are multiple ways you can try it: via the web UI, via cURL, via the command line, or via the Xinference’s python client. Check out our docs for the guide.
For agents
This page has a .md twin and JSON over the API.