Skip to main content

No project description provided

Project description

vLLM TPU vLLM TPU

| Documentation | Blog | User Forum | Developer Slack |


Upcoming Events 🔥

Latest News 🔥

  • [2025/10] vLLM TPU: A New Unified Backend Supporting PyTorch and JAX on TPU
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 list of recommended models and features that are validated for accuracy and stress-tested for performance.

Get started

Get started with vLLM on TPUs by following the quickstart guide.

Visit our documentation to learn more.

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-0.11.1rc1.tar.gz (230.0 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-0.11.1rc1-py3-none-any.whl (269.0 kB view details)

Uploaded Python 3

File details

Details for the file tpu_inference-0.11.1rc1.tar.gz.

File metadata

  • Download URL: tpu_inference-0.11.1rc1.tar.gz
  • Upload date:
  • Size: 230.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for tpu_inference-0.11.1rc1.tar.gz
Algorithm Hash digest
SHA256 3387d571a8c0ab22eb01ad51e381e1e1ae855f596f93e717024fbd8f7efddf17
MD5 c57f6d98a06ef4bebbf42c8c91b6bb4b
BLAKE2b-256 da47e8fd2911cf1b5d8ca0642111eb3e4f63068298b31cb3142a31b84d57878c

See more details on using hashes here.

File details

Details for the file tpu_inference-0.11.1rc1-py3-none-any.whl.

File metadata

File hashes

Hashes for tpu_inference-0.11.1rc1-py3-none-any.whl
Algorithm Hash digest
SHA256 280496f12bf70da25c03759bd38558c2e67437b54dfbd93e1ca2760d3048bad0
MD5 f1a7eda774f9e4213144dc8d8c676cf7
BLAKE2b-256 10125e1772eec6e7ef0f060a3e5bd7ba2e0ce077f0f7d25ed6d74713fabe5e4f

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