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About
vLLM TPU is now powered by tpu-inference, an expressive and powerful new hardware plugin unifying JAX and PyTorch under a single lowering path within the vLLM project. The new backend now provides a framework for developers to:
- Push the limits of TPU hardware performance in open source.
- Provide more flexibility to JAX and PyTorch users by running PyTorch model definitions performantly on TPU without any additional code changes, while also extending native support to JAX.
- Retain vLLM standardization: keep the same user experience, telemetry, and interface.
Recommended models and features
Although vLLM TPU’s new unified backend makes out-of-the-box high performance serving possible with any model supported in vLLM, the reality is that we're still in the process of implementing a few core components.
For this reason, we’ve provided a Recommended Models and Features page detailing the models and features that are validated through unit, integration, and performance testing.
Get started
Get started with vLLM on TPUs by following the quickstart guide.
Visit our documentation to learn more.
Compatible TPU Generations
- Recommended: v5e, v6e
- Experimental: v3, v4, v5p
Check out a few v6e recipes here!
Contribute
We're always looking for ways to partner with the community to accelerate vLLM TPU development. If you're interested in contributing to this effort, check out the Contributing guide and Issues to start. We recommend filtering Issues on the good first issue tag if it's your first time contributing.
Contact us
- For technical questions and feature requests, open a GitHub Issue
- For feature requests, please open one on Github here
- For discussing with fellow users, use the TPU support topic in the vLLM Forum
- For coordinating contributions and development, use the Developer Slack
- For collaborations and partnerships, contact us at vllm-tpu@google.com
Release files for tpu-inference 0.11.1.dev202511260843
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tpu_inference-0.11.1.dev202511260843.tar.gz | 359.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tpu_inference-0.11.1.dev202511260843-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 789.6 kB
Release files / tpu_inference-0.11.1.dev202511260843.tar.gz
| Download URL | tpu_inference-0.11.1.dev202511260843.tar.gz |
|---|---|
| Size | 359.8 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Transparency logRelease files / tpu_inference-0.11.1.dev202511260843-py3-none-any.whl
| Download URL | tpu_inference-0.11.1.dev202511260843-py3-none-any.whl |
|---|---|
| Size | 429.8 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Nov 26, 2025.
Transparency log
