<p align="center">
   <!-- This image will ONLY show up in GitHub's dark mode -->
  <img src="docs/assets/tpu_inference_dark_mode_short.png#gh-dark-mode-only" alt="vLLM TPU" style="width: 86%;">
    <!-- This image will ONLY show up in GitHub's light mode (and on other platforms) -->
  <img src="docs/assets/tpu_inference_light_mode_short.png#gh-light-mode-only" alt="vLLM TPU" style="width: 86%;">
</p>

<p align="center">
| <a href="https://docs.vllm.ai/projects/tpu/en/latest/"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://discuss.vllm.ai/c/hardware-support/google-tpu-support/27"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a>  (#sig-tpu) |
</p>

---

<p>
  <b>🤝 Contribute to the Project</b><br>
  <sub><i>Looking to help? Click a badge below to find issues that need your attention.</i></sub>
</p>

<!-- START: issue_badges -->
[![bug](https://img.shields.io/badge/bug-12-d73a4a?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22bug%22) [![good first issue](https://img.shields.io/badge/good%20first%20issue-8-7057ff?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22good%20first%20issue%22) [![enhancement](https://img.shields.io/badge/enhancement-7-a2eeef?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22enhancement%22) [![contribution-welcome](https://img.shields.io/badge/contribution--welcome-5-ededed?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22contribution-welcome%22) [![auto-generated](https://img.shields.io/badge/auto--generated-5-ededed?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22auto-generated%22) [![View All Issues](https://img.shields.io/badge/View%20All%20Issues-184-238636?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues)
<!-- END: issue_badges -->

## Latest News

- [Announcing Gemma 4 on vLLM](https://vllm.ai/blog/gemma4) Byte for byte, the most capable open models - available on TPUs on Day 0!

<details markdown="1">
<summary><i>Previous News</i> 🔥</summary>

- [Pytorch Conference](https://pytorchconference.sched.com/event/27QCh/sponsored-session-everything-everywhere-all-at-once-vllm-hardware-optionality-with-spotify-and-google-brittany-rockwell-google-shireen-kheradpey-spotify) Learn how Spotify uses vLLM with both GPUs and TPUs to drive down costs and improve user experience.
- [Ray Summit, November 3-5](https://www.anyscale.com/ray-summit/2025) in San Francisco!
- [JAX DevLab on November 18th](https://rsvp.withgoogle.com/events/devlab-fall-2025) in Sunnyvale!
- [2025/10] [vLLM TPU: A New Unified Backend Supporting PyTorch and JAX on TPU](https://blog.vllm.ai/2025/10/16/vllm-tpu.html)

</details>

<br>

## 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.
<br>

## 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](https://docs.vllm.ai/projects/tpu/en/latest/recommended_models_features/)** page detailing the models and features that are validated through unit, integration, and performance testing.

<br>

## Get started

Get started with vLLM on TPUs by following the [quickstart guide](https://docs.vllm.ai/projects/tpu/en/latest/getting_started/quickstart/).

Visit our [documentation](https://docs.vllm.ai/projects/tpu/en/latest/) to learn more.

**Compatible TPU Generations**
- Recommended: v7x, v5e, v6e
- Experimental: v3, v4, v5p

<br>

## Recipes

- [v7x (Ironwood) Recipes](https://github.com/AI-Hypercomputer/tpu-recipes/tree/main/inference/ironwood/vLLM)
- [v6e (Trillium) Recipes](https://github.com/AI-Hypercomputer/tpu-recipes/tree/main/inference/trillium/vLLM)

<br>

## TPU Support Matrix Dashboard

Below is the live status of our supported models, features, and kernels. Click on any category to expand the detailed support table. It is automatically updated from our detailed [Support Matrices](https://github.com/vllm-project/tpu-inference/tree/main/support_matrices).

*Last Updated: 2026-07-02 08:07 AM UTC*

<details open markdown="1">
<summary> <b>🚦 <i>Status Legend</i> </b> </summary>

> - ✅ **Passing:** Tested and works as expected. Ready for use.
> - ❌ **Failing:** Known to be broken or not functional. Help is wanted to fix this!
> - 🧪 **Experimental:** Works, but unoptimized or pending community validation.
> - 📝 **Planned:** Not yet implemented, but on the official roadmap.
> - ⛔️ **Unplanned:** There is no benefit to adding this.
> - ❓ **Untested:** The functionality exists but has not been recently or thoroughly verified.
>
> <details>
> <summary> <b>📐 <i>View Matrix Aggregation Rules (v6e/v7x & C+P)</i></b> </summary>
>
> - **🛠️ Correctness + Performance (C + P)**
>   - ❌ **Failing**: If either check fails.
>   - ✅ **Passing**: If **BOTH** checks pass successfully.
>   - ❓ **Untested**: If any check is untested (and neither fails).
>
> - **🌐 Hardware Rollups (v6e + v7x)**
>   - ❌ **Failing**: If the feature fails on **either** v6e or v7x.
>   - ✅ **Passing**: If the feature passes on **BOTH** v6e and v7x.
>   - ❓ **Untested**: If either generation is untested (and neither fails).
> </details>

</details>

<br>

### Release Support Matrices

<details open markdown="1">
<summary><b>Click to expand support matrices</b></summary>

<blockquote>

<i>Stable support status for official releases and production deployments.</i><br><br>

<details open markdown="1">
<summary><b> ✅ Tested Models </b></summary>

<!-- START: release_model_support -->
| Model | Type | Unit&nbsp;Test | Correctness&nbsp;Test | Performance&nbsp;Test |
| --- | --- | --- | --- | --- |
| [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> |
| [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> |
| [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> |
| [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | Embedding | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [moonshotai/Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> | <span title="❓ Untested">❓</span> |
| [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-Math-V2](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.2-Speciale](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [moonshotai/Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |

<!-- END: release_model_support -->

</details>

<details open markdown="1">
<summary><b> 🚀&nbsp; Advanced Capabilities </b></summary>
<blockquote>

<details open markdown="1">
<summary>Core Features</summary>

<!-- START: release_core_features -->
<table>
  <thead>
    <tr>
      <th>Feature</th>
      <th>Flax</th>
      <th>Torchax</th>
      <th>Default</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>async scheduler</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Chunked Prefill</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>DCN-based P/D disaggregation</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>KV Cache Offload</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>LoRA_Torch</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Multimodal Inputs</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Out-of-tree model support</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Prefix Caching</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Single Program Multi Data</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: Eagle3</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: DFlash</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: Ngram</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>hybrid kv cache</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>multi-host</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>runai_model_streamer_loader</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>sampling_params</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>Step Pooling (Embedding)</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>structured_decoding</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>
<!-- END: release_core_features -->

</details>

<details open markdown="1">
<summary>Parallelism Techniques</summary>

<!-- START: release_parallelism -->
<table>
  <thead>
    <tr>
      <th rowspan="2" width="150" style="text-align:left">Feature</th>
      <th colspan="2">Flax</th>
      <th colspan="2">Torchax</th>
    </tr>
    <tr>
      <th>Single-host</th>
      <th>Multi-host</th>
      <th>Single-host</th>
      <th>Multi-host</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>PP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>DP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>EP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>TP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>CP</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>SP&nbsp;(<a href="https://github.com/vllm-project/tpu-inference/issues/1749">vote&nbsp;to&nbsp;prioritize</a>)</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>
<!-- END: release_parallelism -->

</details>

<details open markdown="1">
<summary>Quantization Methods</summary>

<!-- START: release_quantization -->
<table>
  <thead>
    <tr>
      <th>Checkpoint dtype</th>
      <th>Method</th>
      <th>Supported<br>Hardware Acceleration</th>
      <th>Flax</th>
      <th>Torchax</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>FP4 W4A16</td>
      <td>mxfp4</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>FP8 W8A16</td>
      <td>compressed-tensor</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>FP8 W8A8</td>
      <td>compressed-tensor</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>INT4 W4A16</td>
      <td>awq</td>
      <td>v5, v6</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>INT8 W8A8</td>
      <td>compressed-tensor</td>
      <td>v5, v6</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>NVFP4 W4A16</td>
      <td>modelopt_fp4</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>

> **Note:**
> - *This table only tests checkpoint loading compatibility.*
<!-- END: release_quantization -->

</details>

</details>

<details open markdown="1">
<summary><b> 🔬 Microbenchmark Kernel Support </b></summary>
<blockquote>

<!-- START: release_microbenchmarks -->
<table>
  <thead>
    <tr>
      <th width="150" style="text-align:left">Category</th>
      <th width="300" style="text-align:left">Test</th>
      <th>W16A16</th>
      <th>W8A8</th>
      <th>W8A16</th>
      <th>W4A4</th>
      <th>W4A8</th>
      <th>W4A16</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="2"><b>Moe</b></td>
      <td>Fused&nbsp;MoE</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>gmm</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
  <tbody>
    <tr>
      <td rowspan="1"><b>Dense</b></td>
      <td>All&#8209;gather&nbsp;matmul</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
  <tbody>
    <tr>
      <td rowspan="3"><b>Attention</b></td>
      <td>Generic&nbsp;Ragged&nbsp;Paged<br>Attention&nbsp;V3*</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>MLA</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>Ragged&nbsp;Paged<br>Attention&nbsp;V3&nbsp;Head_Dim<br>64*</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>

> **Note:**
> - *For attention kernels, W[x]A[y] denotes KV cache as W, A as compute, and x, y as bit precision.*
<!-- END: release_microbenchmarks -->

</details>

</blockquote>
</details>

<br>

### Nightly Support Matrices

<details markdown="1">
<summary><b>Click to expand support matrices</b></summary>

<blockquote>

<i>Support status for the latest nightly/main branch developments.</i><br><br>

<details open markdown="1">
<summary><b> ✅ Tested Models </b></summary>

<!-- START: nightly_model_support -->
| Model | Type | Unit&nbsp;Test | Correctness&nbsp;Test | Performance&nbsp;Test |
| --- | --- | --- | --- | --- |
| [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen3.5-397B-A17B](https://huggingface.co/Qwen/Qwen3.5-397B-A17B) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> |
| [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | Multimodal | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> |
| [Qwen/Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | Embedding | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [moonshotai/Kimi-K2.6](https://huggingface.co/moonshotai/Kimi-K2.6) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | Text | <span title="✅ Passing">✅</span> | <span title="✅ Passing">✅</span> | <span title="❓ Untested">❓</span> |
| [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> | <span title="❓ Untested">❓</span> |
| [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) | Multimodal | <span title="✅ Passing">✅</span> | <span title="❌ Failing">❌</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | Text | <span title="❌ Failing">❌</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | Multimodal | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-Math-V2](https://huggingface.co/deepseek-ai/DeepSeek-Math-V2) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.1](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [deepseek-ai/DeepSeek-V3.2-Speciale](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Speciale) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [moonshotai/Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |
| [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5) | Text | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> | <span title="❓ Untested">❓</span> |

<!-- END: nightly_model_support -->

</details>

<details open markdown="1">
<summary><b> 🚀&nbsp; Advanced Capabilities </b></summary>
<blockquote>

<details open markdown="1">
<summary>Core Features</summary>

<!-- START: nightly_core_features -->
<table>
  <thead>
    <tr>
      <th>Feature</th>
      <th>Flax</th>
      <th>Torchax</th>
      <th>Default</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>async scheduler</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Chunked Prefill</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>DCN-based P/D disaggregation</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>KV Cache Offload</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>LoRA_Torch</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Multimodal Inputs</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Out-of-tree model support</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Prefix Caching</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Single Program Multi Data</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: Eagle3</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: DFlash</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>Speculative Decoding: Ngram</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>hybrid kv cache</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>multi-host</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>runai_model_streamer_loader</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>sampling_params</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>Step Pooling (Embedding)</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>structured_decoding</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>
<!-- END: nightly_core_features -->

</details>

<details open markdown="1">
<summary>Parallelism Techniques</summary>

<!-- START: nightly_parallelism -->
<table>
  <thead>
    <tr>
      <th rowspan="2" width="150" style="text-align:left">Feature</th>
      <th colspan="2">Flax</th>
      <th colspan="2">Torchax</th>
    </tr>
    <tr>
      <th>Single-host</th>
      <th>Multi-host</th>
      <th>Single-host</th>
      <th>Multi-host</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>PP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
    </tr>
    <tr>
      <td>DP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>EP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>TP</td>
      <td><span title="✅&nbsp;Passing">✅</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❌&nbsp;Failing">❌</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>CP</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>SP&nbsp;(<a href="https://github.com/vllm-project/tpu-inference/issues/1749">vote&nbsp;to&nbsp;prioritize</a>)</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>
<!-- END: nightly_parallelism -->

</details>

<details open markdown="1">
<summary>Quantization Methods</summary>

<!-- START: nightly_quantization -->
<table>
  <thead>
    <tr>
      <th>Checkpoint dtype</th>
      <th>Method</th>
      <th>Supported<br>Hardware Acceleration</th>
      <th>Flax</th>
      <th>Torchax</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>FP4 W4A16</td>
      <td>mxfp4</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>FP8 W8A16</td>
      <td>compressed-tensor</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>FP8 W8A8</td>
      <td>compressed-tensor</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>INT4 W4A16</td>
      <td>awq</td>
      <td>v5, v6</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>INT8 W8A8</td>
      <td>compressed-tensor</td>
      <td>v5, v6</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>NVFP4 W4A16</td>
      <td>modelopt_fp4</td>
      <td>v7</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>

> **Note:**
> - *This table only tests checkpoint loading compatibility.*
<!-- END: nightly_quantization -->

</details>

</details>

<details open markdown="1">
<summary><b> 🔬 Microbenchmark Kernel Support </b></summary>
<blockquote>

<!-- START: nightly_microbenchmarks -->
<table>
  <thead>
    <tr>
      <th width="150" style="text-align:left">Category</th>
      <th width="300" style="text-align:left">Test</th>
      <th>W16A16</th>
      <th>W8A8</th>
      <th>W8A16</th>
      <th>W4A4</th>
      <th>W4A8</th>
      <th>W4A16</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="2"><b>Moe</b></td>
      <td>Fused&nbsp;MoE</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>gmm</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
  <tbody>
    <tr>
      <td rowspan="1"><b>Dense</b></td>
      <td>All&#8209;gather&nbsp;matmul</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
  <tbody>
    <tr>
      <td rowspan="3"><b>Attention</b></td>
      <td>Generic&nbsp;Ragged&nbsp;Paged<br>Attention&nbsp;V3*</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>MLA</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
    <tr>
      <td>Ragged&nbsp;Paged<br>Attention&nbsp;V3&nbsp;Head_Dim<br>64*</td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
      <td><span title="❓&nbsp;Untested">❓</span></td>
    </tr>
  </tbody>
</table>

> **Note:**
> - *For attention kernels, W[x]A[y] denotes KV cache as W, A as compute, and x, y as bit precision.*
<!-- END: nightly_microbenchmarks -->

</details>

</blockquote>
</details>

<br>

## 🤝 Contribute

<!-- START: issue_badges -->
[![bug](https://img.shields.io/badge/bug-12-d73a4a?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22bug%22) [![good first issue](https://img.shields.io/badge/good%20first%20issue-8-7057ff?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22good%20first%20issue%22) [![enhancement](https://img.shields.io/badge/enhancement-7-a2eeef?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22enhancement%22) [![contribution-welcome](https://img.shields.io/badge/contribution--welcome-5-ededed?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22contribution-welcome%22) [![auto-generated](https://img.shields.io/badge/auto--generated-5-ededed?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22auto-generated%22) [![View All Issues](https://img.shields.io/badge/View%20All%20Issues-184-238636?style=flat-square)](https://github.com/vllm-project/tpu-inference/issues)
<!-- END: issue_badges -->

We're thrilled you're interested in contributing to the vLLM TPU project! Your help is essential for making our tools better for everyone. There are many ways to get involved, even if you're not ready to write code.

**Ways to Contribute:**

- **🐞 Submit Bugs & Suggest Features:** See an issue or have an idea? Open a [new issue](https://github.com/vllm-project/tpu-inference/issues/new/choose) to let us know.
- **👀 Provide Feedback on Pull Requests:** Lend your expertise by reviewing [open pull requests](https://github.com/vllm-project/tpu-inference/pulls) and helping us improve the quality of our codebase.
- **📚 Improve Our Documentation:** Help us make our guides clearer. Fix a typo, clarify a confusing section, or write a new recipe.

If you're ready to contribute code, our **[Contributing Guide](https://github.com/vllm-project/tpu-inference/blob/main/CONTRIBUTING.md)** is the best place to start. It covers everything you need to know, including:

- **Tips for finding an issue to work on** (we recommend starting with our **[good-first issues](https://github.com/vllm-project/tpu-inference/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)**!.

<br>

## 🌟 Contributors Wall

A huge thank you to everyone who has helped build and improve `vllm-project/tpu-inference`!

<details markdown="1">
<summary><b>🌟 <i>Contribution Type Legend & Ranking</i></b></summary>

> | Emoji | Contribution | Meaning |
> | :---: | :--- | :--- |
> | 💻 | **Code** | Submitted merged pull requests or code changes. |
> | 🐛 | **Issues** | Opened valid issues or bug reports. |
> | 👀 | **Reviews** | Reviewed pull requests and provided feedback. |

<br>

**🏆 Ranking:** Contributors are sorted from highest to lowest based on their total effort score (`Total Commits + Unique Issues Opened + PRs Reviewed`). If there is a tie, contributors are displayed alphabetically.

</details>

<br>

<!-- START: contributors -->
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      <td align="center" valign="top" width="14.28%"><a href="https://github.com/bzgoogle"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/198827084?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="bzgoogle"/><br /><sub><b>bzgoogle</b></sub></a><br /><a href="https://github.com/bzgoogle" title="Contributions">👀 💻</a></td>
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<details markdown="1">
<summary><b>...and more! Click to view all contributors.</b></summary>
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      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AahilA"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/44123487?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="AahilA"/><br /><sub><b>AahilA</b></sub></a><br /><a href="https://github.com/AahilA" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/amishacorns"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/13968559?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="amishacorns"/><br /><sub><b>amishacorns</b></sub></a><br /><a href="https://github.com/amishacorns" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/carlesoctav"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/106587439?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="carlesoctav"/><br /><sub><b>carlesoctav</b></sub></a><br /><a href="https://github.com/carlesoctav" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/dannikay"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/48867745?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="dannikay"/><br /><sub><b>dannikay</b></sub></a><br /><a href="https://github.com/dannikay" title="Contributions">💻</a></td>
    </tr>
    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/depksingh"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/217023309?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="depksingh"/><br /><sub><b>depksingh</b></sub></a><br /><a href="https://github.com/depksingh" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Dineshkumar-Anandan-ZS0367"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/105219055?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="Dineshkumar-Anandan-ZS0367"/><br /><sub><b>Dineshkumar-Anandan-ZS0367</b></sub></a><br /><a href="https://github.com/Dineshkumar-Anandan-ZS0367" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/dtrifiro"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/36171005?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="dtrifiro"/><br /><sub><b>dtrifiro</b></sub></a><br /><a href="https://github.com/dtrifiro" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/erfanzar"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/59269023?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="erfanzar"/><br /><sub><b>erfanzar</b></sub></a><br /><a href="https://github.com/erfanzar" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/inho9606"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/29620436?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="inho9606"/><br /><sub><b>inho9606</b></sub></a><br /><a href="https://github.com/inho9606" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/jk1333"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/17493839?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="jk1333"/><br /><sub><b>jk1333</b></sub></a><br /><a href="https://github.com/jk1333" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/jyj0w0"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/27630668?u=9bd1c8c42d174a99cc37ae8eb36e2167b624e7e8&v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="jyj0w0"/><br /><sub><b>jyj0w0</b></sub></a><br /><a href="https://github.com/jyj0w0" title="Contributions">👀</a></td>
    </tr>
    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/kuafou"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/41641871?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="kuafou"/><br /><sub><b>kuafou</b></sub></a><br /><a href="https://github.com/kuafou" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/kyle-google"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/111800332?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="kyle-google"/><br /><sub><b>kyle-google</b></sub></a><br /><a href="https://github.com/kyle-google" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Mhdaw"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/164439157?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="Mhdaw"/><br /><sub><b>Mhdaw</b></sub></a><br /><a href="https://github.com/Mhdaw" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/mokeddembillel"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/25545242?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="mokeddembillel"/><br /><sub><b>mokeddembillel</b></sub></a><br /><a href="https://github.com/mokeddembillel" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/oindrila-b"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/53270901?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="oindrila-b"/><br /><sub><b>oindrila-b</b></sub></a><br /><a href="https://github.com/oindrila-b" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/oliverdutton"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/44170519?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="oliverdutton"/><br /><sub><b>oliverdutton</b></sub></a><br /><a href="https://github.com/oliverdutton" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/pathfinder-pf"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/230268798?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="pathfinder-pf"/><br /><sub><b>pathfinder-pf</b></sub></a><br /><a href="https://github.com/pathfinder-pf" title="Contributions">🐛</a></td>
    </tr>
    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/piotrfrankowski"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/17426499?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="piotrfrankowski"/><br /><sub><b>piotrfrankowski</b></sub></a><br /><a href="https://github.com/piotrfrankowski" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/reeaz27-droid"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/245602856?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="reeaz27-droid"/><br /><sub><b>reeaz27-droid</b></sub></a><br /><a href="https://github.com/reeaz27-droid" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/rupeng-liu"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/242684140?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="rupeng-liu"/><br /><sub><b>rupeng-liu</b></sub></a><br /><a href="https://github.com/rupeng-liu" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/salmanmohammadi"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/25081738?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="salmanmohammadi"/><br /><sub><b>salmanmohammadi</b></sub></a><br /><a href="https://github.com/salmanmohammadi" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/vlad-karp"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/210436218?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="vlad-karp"/><br /><sub><b>vlad-karp</b></sub></a><br /><a href="https://github.com/vlad-karp" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/XMaster96"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/28674439?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="XMaster96"/><br /><sub><b>XMaster96</b></sub></a><br /><a href="https://github.com/XMaster96" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/yixinshi"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/29932260?u=a7bc68ebf1bcb7ce766e239a2c4d9c263931322b&v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="yixinshi"/><br /><sub><b>yixinshi</b></sub></a><br /><a href="https://github.com/yixinshi" title="Contributions">👀</a></td>
    </tr>
    <tr>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/yuyanpeng-google"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/193563974?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="yuyanpeng-google"/><br /><sub><b>yuyanpeng-google</b></sub></a><br /><a href="https://github.com/yuyanpeng-google" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/zixi-qi"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/22851944?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="zixi-qi"/><br /><sub><b>zixi-qi</b></sub></a><br /><a href="https://github.com/zixi-qi" title="Contributions">💻</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/zongweiz"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/5266615?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="zongweiz"/><br /><sub><b>zongweiz</b></sub></a><br /><a href="https://github.com/zongweiz" title="Contributions">🐛</a></td>
      <td align="center" valign="top" width="14.28%"><a href="https://github.com/zzzwen"><img src="https://images.weserv.nl/?url=https://avatars.githubusercontent.com/u/1835075?v=4&s=100&w=90&h=90&fit=cover&mask=circle&maxage=7d" width="45" height="45" alt="zzzwen"/><br /><sub><b>zzzwen</b></sub></a><br /><a href="https://github.com/zzzwen" title="Contributions">💻</a></td>
    </tr>
  </tbody>
</table>
</details>

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<br>

## 💬&nbsp; Contact us  

- For technical questions and feature requests, open a GitHub [Issue](https://github.com/vllm-project/tpu-inference/issues)
- For feature requests, please open one on Github [here](https://github.com/vllm-project/tpu-inference/issues/new/choose)
- For discussing with fellow users, use the [TPU support topic in the vLLM Forum](https://discuss.vllm.ai/c/hardware-support/google-tpu-support/27)
- For coordinating contributions and development, use the [Developer Slack](https://join.slack.com/share/enQtOTY2OTUxMDIyNjY1OS00M2MxYWQwZjAyMGZjM2MyZjRjNTA0ZjRkNjkzOTRhMzg0NDM2OTlkZDAxOTAzYmJmNzdkNDc4OGZjYTUwMmRh)
- For collaborations and partnerships, contact us at [vllm-tpu@google.com](mailto:vllm-tpu@google.com)
