tpu-inference

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vLLM TPU

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[2025/10] vLLM TPU: A New Unified Backend Supporting PyTorch and JAX on TPU

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 Recommended Models and Recommended Features pages 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: v7x, v5e, v6e
Experimental: v3, v4, v5p

Recipes

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.

Last Updated: 2026-08-27 07:02 AM UTC

🚦 Status Legend

✅ 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.

📐 View Matrix Aggregation Rules (v6e/v7x & C+P)

🛠️ 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).

Release Support Matrices

Click to expand support matrices

Stable support status for official releases and production deployments.

✅ Tested Models

Model Type Unit Test Correctness Test Performance Test

google/gemma-4-26B-A4B-it Multimodal ✅ ✅ ✅

google/gemma-4-31B-it Multimodal ✅ ✅ ✅

google/gemma-4-E2B-it Multimodal ✅ ✅ ✅

google/gemma-4-E4B-it Multimodal ✅ ✅ ✅

google/gemma-3-27b-it Text ✅ ✅ ✅

meta-llama/Llama-3.1-8B-Instruct Text ✅ ✅ ✅

meta-llama/Llama-3.3-70B-Instruct Text ✅ ✅ ✅

Qwen/Qwen3-30B-A3B Text ✅ ✅ ✅

Qwen/Qwen3-32B Text ✅ ✅ ✅

Qwen/Qwen3-4B Text ✅ ✅ ✅

Qwen/Qwen3-Coder-480B-A35B-Instruct Text ✅ ✅ ✅

Qwen/Qwen3.5-397B-A17B Text ✅ ✅ ✅

Qwen/Qwen3-Embedding-8B Embedding ✅ ✅ ❓

Qwen/Qwen2.5-VL-7B-Instruct Multimodal ✅ ✅ ❓

deepseek-ai/DeepSeek-R1 Text ✅ ✅ ❓

moonshotai/Kimi-K2.6 Text ✅ ✅ ❓

openai/gpt-oss-120b Text ✅ ✅ ❓

deepseek-ai/DeepSeek-OCR Multimodal ❓ ❓ ❓

meta-llama/Llama-4-Maverick-17B-128E-Instruct/multimodal Multimodal ❓ ❓ ❓

Qwen/Qwen3-Omni-30B-A3B-Instruct Multimodal ❓ ❓ ❓

Qwen/Qwen3-VL-8B-Instruct Multimodal ❓ ❓ ❓

Qwen/Qwen3.5-9B Multimodal ❓ ❓ ❓

deepseek-ai/DeepSeek-Math-V2 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.1 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.2 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.2-Speciale Text ❓ ❓ ❓

MiniMaxAI/MiniMax-M2.5 Text ❓ ❓ ❓

mistralai/Mistral-Small-4-119B-2603/text-only Text ❓ ❓ ❓

moonshotai/Kimi-K2-Thinking Text ❓ ❓ ❓

openai/gpt-oss-20b Text ❓ ❓ ❓

zai-org/GLM-5 Text ❓ ❓ ❓

🚀 Advanced Capabilities

Core Features

Feature Flax Torchax Default

async scheduler ✅ ✅ ✅

Chunked Prefill ✅ ✅ ✅

DCN-based P/D disaggregation ✅ ✅ ✅

KV Cache Offload ✅ ✅ ✅

LoRA_Torch ✅ ✅ ✅

Multimodal Inputs ✅ ✅ ✅

Out-of-tree model support ✅ ✅ ✅

Prefix Caching ✅ ✅ ✅

Single Program Multi Data ✅ ✅ ✅

Speculative Decoding: Eagle3 ✅ ✅ ✅

Speculative Decoding: Ngram ✅ ✅ ✅

Speculative Decoding: DFlash ✅ ❓ ✅

hybrid kv cache ❓ ❓ ❓

multi-host ❓ ❓ ❓

runai_model_streamer_loader ❓ ❓ ❓

sampling_params ❓ ❓ ❓

Step Pooling (Embedding) ❓ ❓ ❓

structured_decoding ❓ ❓ ❓

Parallelism Techniques

Feature Flax Torchax

Single-host Multi-host Single-host Multi-host

PP ✅ ✅ ✅ ✅

DP ✅ ❓ ✅ ❓

EP ✅ ❓ ✅ ❓

TP ✅ ❓ ✅ ❓

CP ❓ ❓ ❓ ❓

SP (vote to prioritize) ❓ ❓ ❓ ❓

Quantization Methods

Checkpoint dtype Method Supported
Hardware Acceleration Flax Torchax

FP4 W4A16 mxfp4 v7 ❓ ❓

FP8 W8A16 compressed-tensor v7 ❓ ❓

FP8 W8A8 compressed-tensor v7 ❓ ❓

INT4 W4A16 awq v5, v6 ❓ ❓

INT8 W8A8 compressed-tensor v5, v6 ❓ ❓

NVFP4 W4A16 modelopt_fp4 v7 ❓ ❓

Note:

This table only tests checkpoint loading compatibility.

🔬 Microbenchmark Kernel Support

Category Test W16A16 W8A8 W8A16 W4A4 W4A8 W4A16

Moe Fused MoE ❓ ❓ ❓ ❓ ❓ ❓

gmm ❓ ❓ ❓ ❓ ❓ ❓

Dense All‑gather matmul ❓ ❓ ❓ ❓ ❓ ❓

Attention Generic Ragged Paged
Attention V3* ❓ ❓ ❓ ❓ ❓ ❓

MLA ❓ ❓ ❓ ❓ ❓ ❓

Ragged Paged
Attention V3 Head_Dim
64* ❓ ❓ ❓ ❓ ❓ ❓

Note:

For attention kernels, W[x]A[y] denotes KV cache as W, A as compute, and x, y as bit precision.

Model	Type	Unit Test	Correctness Test	Performance Test
google/gemma-4-26B-A4B-it	Multimodal	✅	✅	✅
google/gemma-4-31B-it	Multimodal	✅	✅	✅
google/gemma-4-E2B-it	Multimodal	✅	✅	✅
google/gemma-4-E4B-it	Multimodal	✅	✅	✅
google/gemma-3-27b-it	Text	✅	✅	✅
meta-llama/Llama-3.1-8B-Instruct	Text	✅	✅	✅
meta-llama/Llama-3.3-70B-Instruct	Text	✅	✅	✅
Qwen/Qwen3-30B-A3B	Text	✅	✅	✅
Qwen/Qwen3-32B	Text	✅	✅	✅
Qwen/Qwen3-4B	Text	✅	✅	✅
Qwen/Qwen3-Coder-480B-A35B-Instruct	Text	✅	✅	✅
Qwen/Qwen3.5-397B-A17B	Text	✅	✅	✅
Qwen/Qwen3-Embedding-8B	Embedding	✅	✅	❓
Qwen/Qwen2.5-VL-7B-Instruct	Multimodal	✅	✅	❓
deepseek-ai/DeepSeek-R1	Text	✅	✅	❓
moonshotai/Kimi-K2.6	Text	✅	✅	❓
openai/gpt-oss-120b	Text	✅	✅	❓
deepseek-ai/DeepSeek-OCR	Multimodal	❓	❓	❓
meta-llama/Llama-4-Maverick-17B-128E-Instruct/multimodal	Multimodal	❓	❓	❓
Qwen/Qwen3-Omni-30B-A3B-Instruct	Multimodal	❓	❓	❓
Qwen/Qwen3-VL-8B-Instruct	Multimodal	❓	❓	❓
Qwen/Qwen3.5-9B	Multimodal	❓	❓	❓
deepseek-ai/DeepSeek-Math-V2	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.1	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.2	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.2-Speciale	Text	❓	❓	❓
MiniMaxAI/MiniMax-M2.5	Text	❓	❓	❓
mistralai/Mistral-Small-4-119B-2603/text-only	Text	❓	❓	❓
moonshotai/Kimi-K2-Thinking	Text	❓	❓	❓
openai/gpt-oss-20b	Text	❓	❓	❓
zai-org/GLM-5	Text	❓	❓	❓

Feature	Flax	Torchax	Default
async scheduler	✅	✅	✅
Chunked Prefill	✅	✅	✅
DCN-based P/D disaggregation	✅	✅	✅
KV Cache Offload	✅	✅	✅
LoRA_Torch	✅	✅	✅
Multimodal Inputs	✅	✅	✅
Out-of-tree model support	✅	✅	✅
Prefix Caching	✅	✅	✅
Single Program Multi Data	✅	✅	✅
Speculative Decoding: Eagle3	✅	✅	✅
Speculative Decoding: Ngram	✅	✅	✅
Speculative Decoding: DFlash	✅	❓	✅
hybrid kv cache	❓	❓	❓
multi-host	❓	❓	❓
runai_model_streamer_loader	❓	❓	❓
sampling_params	❓	❓	❓
Step Pooling (Embedding)	❓	❓	❓
structured_decoding	❓	❓	❓

Feature	Flax	Torchax
PP	✅	✅	✅	✅
DP	✅	❓	✅	❓
EP	✅	❓	✅	❓
TP	✅	❓	✅	❓
CP	❓	❓	❓	❓
SP (vote to prioritize)	❓	❓	❓	❓

Checkpoint dtype	Method	Supported Hardware Acceleration	Flax	Torchax
FP4 W4A16	mxfp4	v7	❓	❓
FP8 W8A16	compressed-tensor	v7	❓	❓
FP8 W8A8	compressed-tensor	v7	❓	❓
INT4 W4A16	awq	v5, v6	❓	❓
INT8 W8A8	compressed-tensor	v5, v6	❓	❓
NVFP4 W4A16	modelopt_fp4	v7	❓	❓

Category	Test	W16A16	W8A8	W8A16	W4A4	W4A8	W4A16
Moe	Fused MoE	❓	❓	❓	❓	❓	❓
gmm	❓	❓	❓	❓	❓	❓
Dense	All‑gather matmul	❓	❓	❓	❓	❓	❓
Attention	Generic Ragged Paged Attention V3*	❓	❓	❓	❓	❓	❓
MLA	❓	❓	❓	❓	❓	❓
Ragged Paged Attention V3 Head_Dim 64*	❓	❓	❓	❓	❓	❓

Nightly Support Matrices

Click to expand support matrices

Support status for the latest nightly/main branch developments.

✅ Tested Models

Model Type Unit Test Correctness Test Performance Test

google/gemma-4-26B-A4B-it Multimodal ✅ ✅ ✅

google/gemma-4-31B-it Multimodal ✅ ✅ ✅

google/gemma-3-27b-it Text ✅ ✅ ✅

meta-llama/Llama-3.1-8B-Instruct Text ✅ ✅ ✅

meta-llama/Llama-3.3-70B-Instruct Text ✅ ✅ ✅

Qwen/Qwen3-30B-A3B Text ✅ ✅ ✅

Qwen/Qwen3-4B Text ✅ ✅ ✅

Qwen/Qwen3-Coder-480B-A35B-Instruct Text ✅ ✅ ✅

Qwen/Qwen3.5-397B-A17B Text ✅ ✅ ✅

Qwen/Qwen2.5-VL-7B-Instruct Multimodal ✅ ✅ ❌

Qwen/Qwen3-Embedding-8B Embedding ✅ ✅ ❓

deepseek-ai/DeepSeek-R1 Text ✅ ✅ ❓

moonshotai/Kimi-K2.6 Text ✅ ✅ ❓

openai/gpt-oss-120b Text ✅ ✅ ❓

google/gemma-4-E2B-it Multimodal ✅ ❌ ❓

google/gemma-4-E4B-it Multimodal ✅ ❌ ❓

Qwen/Qwen3-32B Text ❌ ❓ ❓

deepseek-ai/DeepSeek-OCR Multimodal ❓ ❓ ❓

Qwen/Qwen3-Omni-30B-A3B-Instruct Multimodal ❓ ❓ ❓

Qwen/Qwen3-VL-8B-Instruct Multimodal ❓ ❓ ❓

Qwen/Qwen3.5-9B Multimodal ❓ ❓ ❓

deepseek-ai/DeepSeek-Math-V2 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.1 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.2 Text ❓ ❓ ❓

deepseek-ai/DeepSeek-V3.2-Speciale Text ❓ ❓ ❓

MiniMaxAI/MiniMax-M2.5 Text ❓ ❓ ❓

moonshotai/Kimi-K2-Thinking Text ❓ ❓ ❓

openai/gpt-oss-20b Text ❓ ❓ ❓

zai-org/GLM-5 Text ❓ ❓ ❓

🚀 Advanced Capabilities

Core Features

Feature Flax Torchax Default

async scheduler ✅ ✅ ✅

Chunked Prefill ✅ ✅ ✅

DCN-based P/D disaggregation ✅ ✅ ✅

KV Cache Offload ✅ ✅ ✅

LoRA_Torch ✅ ✅ ✅

Multimodal Inputs ✅ ✅ ✅

Out-of-tree model support ✅ ✅ ✅

Prefix Caching ✅ ✅ ✅

Single Program Multi Data ✅ ✅ ✅

Speculative Decoding: Eagle3 ✅ ✅ ✅

Speculative Decoding: DFlash ✅ ✅ ✅

Speculative Decoding: Ngram ✅ ✅ ✅

hybrid kv cache ❓ ❓ ❓

multi-host ❓ ❓ ❓

runai_model_streamer_loader ❓ ❓ ❓

sampling_params ❓ ❓ ❓

Step Pooling (Embedding) ❓ ❓ ❓

structured_decoding ❓ ❓ ❓

Parallelism Techniques

Feature Flax Torchax

Single-host Multi-host Single-host Multi-host

PP ✅ ✅ ✅ ✅

DP ✅ ❓ ✅ ❓

EP ✅ ❓ ✅ ❓

TP ✅ ❓ ❌ ❓

CP ❓ ❓ ❓ ❓

SP (vote to prioritize) ❓ ❓ ❓ ❓

Quantization Methods

Checkpoint dtype Method Supported
Hardware Acceleration Flax Torchax

FP4 W4A16 mxfp4 v7 ❓ ❓

FP8 W8A16 compressed-tensor v7 ❓ ❓

FP8 W8A8 compressed-tensor v7 ❓ ❓

INT4 W4A16 awq v5, v6 ❓ ❓

INT8 W8A8 compressed-tensor v5, v6 ❓ ❓

NVFP4 W4A16 modelopt_fp4 v7 ❓ ❓

Note:

This table only tests checkpoint loading compatibility.

🔬 Microbenchmark Kernel Support

Category Test W16A16 W8A8 W8A16 W4A4 W4A8 W4A16

Moe Fused MoE ❓ ❓ ❓ ❓ ❓ ❓

gmm ❓ ❓ ❓ ❓ ❓ ❓

Dense All‑gather matmul ❓ ❓ ❓ ❓ ❓ ❓

Attention Generic Ragged Paged
Attention V3* ❓ ❓ ❓ ❓ ❓ ❓

MLA ❓ ❓ ❓ ❓ ❓ ❓

Ragged Paged
Attention V3 Head_Dim
64* ❓ ❓ ❓ ❓ ❓ ❓

Note:

For attention kernels, W[x]A[y] denotes KV cache as W, A as compute, and x, y as bit precision.

Model	Type	Unit Test	Correctness Test	Performance Test
google/gemma-4-26B-A4B-it	Multimodal	✅	✅	✅
google/gemma-4-31B-it	Multimodal	✅	✅	✅
google/gemma-3-27b-it	Text	✅	✅	✅
meta-llama/Llama-3.1-8B-Instruct	Text	✅	✅	✅
meta-llama/Llama-3.3-70B-Instruct	Text	✅	✅	✅
Qwen/Qwen3-30B-A3B	Text	✅	✅	✅
Qwen/Qwen3-4B	Text	✅	✅	✅
Qwen/Qwen3-Coder-480B-A35B-Instruct	Text	✅	✅	✅
Qwen/Qwen3.5-397B-A17B	Text	✅	✅	✅
Qwen/Qwen2.5-VL-7B-Instruct	Multimodal	✅	✅	❌
Qwen/Qwen3-Embedding-8B	Embedding	✅	✅	❓
deepseek-ai/DeepSeek-R1	Text	✅	✅	❓
moonshotai/Kimi-K2.6	Text	✅	✅	❓
openai/gpt-oss-120b	Text	✅	✅	❓
google/gemma-4-E2B-it	Multimodal	✅	❌	❓
google/gemma-4-E4B-it	Multimodal	✅	❌	❓
Qwen/Qwen3-32B	Text	❌	❓	❓
deepseek-ai/DeepSeek-OCR	Multimodal	❓	❓	❓
Qwen/Qwen3-Omni-30B-A3B-Instruct	Multimodal	❓	❓	❓
Qwen/Qwen3-VL-8B-Instruct	Multimodal	❓	❓	❓
Qwen/Qwen3.5-9B	Multimodal	❓	❓	❓
deepseek-ai/DeepSeek-Math-V2	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.1	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.2	Text	❓	❓	❓
deepseek-ai/DeepSeek-V3.2-Speciale	Text	❓	❓	❓
MiniMaxAI/MiniMax-M2.5	Text	❓	❓	❓
moonshotai/Kimi-K2-Thinking	Text	❓	❓	❓
openai/gpt-oss-20b	Text	❓	❓	❓
zai-org/GLM-5	Text	❓	❓	❓

Feature	Flax	Torchax	Default
async scheduler	✅	✅	✅
Chunked Prefill	✅	✅	✅
DCN-based P/D disaggregation	✅	✅	✅
KV Cache Offload	✅	✅	✅
LoRA_Torch	✅	✅	✅
Multimodal Inputs	✅	✅	✅
Out-of-tree model support	✅	✅	✅
Prefix Caching	✅	✅	✅
Single Program Multi Data	✅	✅	✅
Speculative Decoding: Eagle3	✅	✅	✅
Speculative Decoding: DFlash	✅	✅	✅
Speculative Decoding: Ngram	✅	✅	✅
hybrid kv cache	❓	❓	❓
multi-host	❓	❓	❓
runai_model_streamer_loader	❓	❓	❓
sampling_params	❓	❓	❓
Step Pooling (Embedding)	❓	❓	❓
structured_decoding	❓	❓	❓

Feature	Flax	Torchax
PP	✅	✅	✅	✅
DP	✅	❓	✅	❓
EP	✅	❓	✅	❓
TP	✅	❓	❌	❓
CP	❓	❓	❓	❓
SP (vote to prioritize)	❓	❓	❓	❓

Checkpoint dtype	Method	Supported Hardware Acceleration	Flax	Torchax
FP4 W4A16	mxfp4	v7	❓	❓
FP8 W8A16	compressed-tensor	v7	❓	❓
FP8 W8A8	compressed-tensor	v7	❓	❓
INT4 W4A16	awq	v5, v6	❓	❓
INT8 W8A8	compressed-tensor	v5, v6	❓	❓
NVFP4 W4A16	modelopt_fp4	v7	❓	❓

Category	Test	W16A16	W8A8	W8A16	W4A4	W4A8	W4A16
Moe	Fused MoE	❓	❓	❓	❓	❓	❓
gmm	❓	❓	❓	❓	❓	❓
Dense	All‑gather matmul	❓	❓	❓	❓	❓	❓
Attention	Generic Ragged Paged Attention V3*	❓	❓	❓	❓	❓	❓
MLA	❓	❓	❓	❓	❓	❓
Ragged Paged Attention V3 Head_Dim 64*	❓	❓	❓	❓	❓	❓

🤝 Contribute

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 to let us know.
👀 Provide Feedback on Pull Requests: Lend your expertise by reviewing open pull requests 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 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!.

🌟 Contributors Wall

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

🌟 Contribution Type Legend & Ranking

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.

Emoji	Contribution	Meaning
💻	Code	Submitted merged pull requests or code changes.
🐛	Issues	Opened valid issues or bug reports.
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Release files for tpu-inference 0.29.0.dev20260923

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Source distribution (sdist)

Source distribution for tpu-inference 0.29.0.dev20260923
File	Size	Uploaded
tpu_inference-0.29.0.dev20260923.tar.gz	1.8 MB	Sep 23, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for tpu-inference 0.29.0.dev20260923
File	Interpreter	ABI	Platform	Reset
tpu_inference-0.29.0.dev20260923-py3-none-any.whl 2.3 MB Sep 23, 2026	Python 3	none	any	Details

Total release size: 4.0 MB

Release files / tpu_inference-0.29.0.dev20260923.tar.gz

Download URL	tpu_inference-0.29.0.dev20260923.tar.gz
Size	1.8 MB
Tags	Source
SHA-256 checksum How to use checksums	`51ee60911e233be64f604c688ab79ba634d7c6ea036b8d83170b12fad4bf7ac0`
BLAKE2b-256 checksum How to use checksums	`390b0aec3a490a30cefdef6e4edd7fec080bba0f94ef6707b862aff90cc290dc`
Upload date	Sep 23, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Publishing platform: GitHub Actions
Publishing repository: github.com/vllm-project/tpu-inference
Publishing commit: github.com/vllm-project/tpu-inference/tree/5c0c45aed9f91e21d4422eba033ed06ac9c2f2c3

Workflow

Publishing configuration: github.com/vllm-project/tpu-inference/blob/5c0c45aed9f91e21d4422eba033ed06ac9c2f2c3/.github/workflows/release.yml
Publishing logs: github.com/vllm-project/tpu-inference/actions/runs/35864548450/attempts/1

Artifact

Subject: tpu_inference-0.29.0.dev20260923.tar.gz
SHA-256 checksum: 51ee60911e233be64f604c688ab79ba634d7c6ea036b8d83170b12fad4bf7ac0 Verifying attestations

Release files / tpu_inference-0.29.0.dev20260923-py3-none-any.whl

Download URL	tpu_inference-0.29.0.dev20260923-py3-none-any.whl
Size	2.3 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`ba2673ff69781ec973b625c6dd72109e51187f6c726e9ba6301f7d1ef02a6e8f`
BLAKE2b-256 checksum How to use checksums	`4e54d4f180c4f3adfbba50a2f6af1134587f1e40d233908f8d82e9fa6042c764`
Upload date	Sep 23, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/vllm-project/tpu-inference
Publishing commit: github.com/vllm-project/tpu-inference/tree/5c0c45aed9f91e21d4422eba033ed06ac9c2f2c3

Workflow

Publishing configuration: github.com/vllm-project/tpu-inference/blob/5c0c45aed9f91e21d4422eba033ed06ac9c2f2c3/.github/workflows/release.yml
Publishing logs: github.com/vllm-project/tpu-inference/actions/runs/35864548450/attempts/1

Artifact

Subject: tpu_inference-0.29.0.dev20260923-py3-none-any.whl
SHA-256 checksum: ba2673ff69781ec973b625c6dd72109e51187f6c726e9ba6301f7d1ef02a6e8f Verifying attestations