Native PyTorch on TPUs is here—and TorchTPU is officially open source.

Train and serve models using the tools you already know: @vllm_project, @sgl_project, @_llm_d_, TorchTitan, @huggingface, and @raydistributed.

Learn more from #PyTorchConf2026 → goo.gle/4hWxEVH

Oct 21, 2026 · 5:00 PM UTC

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Replying to @googlecloud
@googlecloud @vllm_project @_llm_d_ @huggingface @raydistributed Excited to join TorchTPU family!

Here is our blog about SGLang-TorchTPU:
lmsys.org/blog/2026-10-2…
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Replying to @googlecloud
@googlecloud @vllm_project @sgl_project @_llm_d_ @huggingface @raydistributed Exciting news for a native and principled PyTorch backend for Google TPUs announced at #PyTorchCon this week!

Also learn more on the PyTorch blog: pytorch.org/blog/introduci…
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Replying to @googlecloud
@googlecloud @vllm_project @sgl_project @_llm_d_ @huggingface @raydistributed Guess "TPUs mean rewriting everything in JAX" is finally retired as an excuse.
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@googlecloud @vllm_project @sgl_project @_llm_d_ @huggingface @raydistributed The `torch.accelerator.current_accelerator()` example is worth stealing: keep device selection out of the model code. pytorch.org/blog/introduci…
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Replying to @googlecloud
@googlecloud @vllm_project @sgl_project @_llm_d_ @huggingface @raydistributed Good to see familiar serving tools on TPUs. We're scoping a BBF evaluation for repeated exact-answer tasks. Which recipe includes cold setup, cache invalidation and fallback costs as well as warm serving? We want a fair full-cost baseline.
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