Skip to main content

No project description provided

Project description

vLLM TPU vLLM TPU

| Documentation | Blog | User Forum | Developer Slack (#sig-tpu) |


Upcoming Events 🔥

Latest News 🔥

Previous News 🔥

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tpu_inference-0.12.0rc1.tar.gz (364.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

tpu_inference-0.12.0rc1-py3-none-any.whl (432.2 kB view details)

Uploaded Python 3

File details

Details for the file tpu_inference-0.12.0rc1.tar.gz.

File metadata

  • Download URL: tpu_inference-0.12.0rc1.tar.gz
  • Upload date:
  • Size: 364.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.9

File hashes

Hashes for tpu_inference-0.12.0rc1.tar.gz
Algorithm Hash digest
SHA256 04bcb1ef7743dd6d6598600274fbe4cc60946ac9b826746131d2d05dff63179a
MD5 eef443c1132309a134411822b27766d9
BLAKE2b-256 7f1bb43b7a17349110226ff700322c115dff1476a5d1134254910ea5e849795f

See more details on using hashes here.

File details

Details for the file tpu_inference-0.12.0rc1-py3-none-any.whl.

File metadata

File hashes

Hashes for tpu_inference-0.12.0rc1-py3-none-any.whl
Algorithm Hash digest
SHA256 7ecd27fb19ca13081679f1309101d75779bd13e8e4a9140e47ca2c44a4c18631
MD5 aea11ed8cb8958525d998f386b465b06
BLAKE2b-256 9a1a86416b712cef57f1f6733fa64a6ad1b03996eeb535197939d40cac56147b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page