Google adds native PyTorch backends for vLLM and SGLang on TPU
10/23/2026 — 10/24, 10:23·2 sources (all)·2 reports (all)
Key takeaways
- Google Developers Blog launched native PyTorch backends for vLLM and SGLang via TorchTPU, letting developers run and tune inference with fam…
Background & analysis
On October 23, 2026, Google Developers Blog published a post announcing native PyTorch backends for vLLM and SGLang, powered by TorchTPU. The release was presented as part of a broader set of PyTorch announcements made that day.
According to the post, the backends were built in collaboration with the respective open-source maintainers: vLLM was developed in partnership with Inferact, and SGLang in partnership with RadixArk. Google said the goal is to let developers run and optimize serving workloads on TPUs using familiar PyTorch tooling, instead of relying on TPU-specific frameworks or porting code away from PyTorch.
The engines include optimized TPU kernel implementations, along with production-ready features such as KV cache management built on TPU Sync and disaggregated serving. Google cited public benchmarks showing state-of-the-art performance and industry-leading cost efficiency on Ironwood TPU. The post also noted that TPUs have been running large-scale AI workloads for years with high performance and efficiency.
As of the publication, Google had announced the availability of the backends and the benchmark claims on its developer blog. The available material does not indicate any later follow-up, third-party verification, or additional detail beyond that announcement.
AI-generated from 1 reports · updated 11 hours ago
Latest turnGoogle announced native PyTorch backends for vLLM and SGLang built on TorchTPU, letting developers serve and tune models on TPU with familiar PyTorch tooling. The engines ship optimized TPU kernels, KV cache management via TPU Sync and disaggregated serving; Google says public benchmarks show leading performance and cost efficiency on Ironwood TPU.
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Google announced native PyTorch backends for vLLM and SGLang built on TorchTPU, letting developers serve and tune models on TPU with familiar PyTorch tooling. The engines ship optimized TPU kernels, KV cache management via TPU Sync and disaggregated serving; Google says public benchmarks show leading performance and cost efficiency on Ironwood TPU.
Google Developers BlogFirst-partyAI score 59TorchTPU, Google Cloud's native PyTorch stack for TPUs, is now open source on GitHub, with an early preview of the TPU Agent Suite released alongside it. Co-developed with Meta's PyTorch team, it adds Cloud TPUs (v5e, v5p, v6e and v7 / Ironwood) as a native backend option next to GPUs and CPUs. Developers can train, fine-tune and serve PyTorch models on TPUs with minimal code changes using standard APIs.
DEV Community · AIAI score 61
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