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TorchTPU

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Headlines · 6

  1. MiniMax H3 community update adds faster generation and runtime integrations

    MiniMax reports that community contributions to its open-weight MiniMax H3 video-generation ecosystem include 8-step reference-conditioned video and audio, 4-step distillation, sparse attention, and reference-token compression. The update also adds serving and workflow integrations for GPU servers, TPUs, and Apple Silicon, bringing the H3 ecosystem index to 11 additions. MiniMax says these changes can make experimentation more affordable, creative control finer, and deployment choices broader, though those benefits remain vendor claims.

    MiniMax_AI (X) · 🔥 0
  2. PyTorch Conference announces native backends for Google TPU and AWS Trainium

    According to the PyTorch Blog, PyTorch Conference North America 2026 drew more than 3,500 attendees to San Jose on October 20–21 for discussions on open intelligence, research, models, infrastructure, hardware and production. The event introduced TorchTPU, an open-source Google and Meta collaboration providing a native PyTorch backend for Google TPUs, and launched an Apache 2.0 public beta of TorchNeuron, a native backend for AWS Trainium. Other updates covered compiler development for IBM Spyre and an optional backend for AMD MI350-series GPUs. TorchTitan also expanded model support across its 5D parallelism stack and added reinforcement learning through TitanRL.

    PyTorch Blog · 🔥 0
  3. SGLang launches open-source PyTorch-native TPU serving backend

    SGLang launched SGLang-TorchTPU, an open-source native PyTorch backend that brings its serving stack to Google Cloud TPU while retaining the same core codebase used for GPUs. The project, developed by RadixArk’s SGLang team with Google and Meta, includes real-TPU CI tests and support for multiple language and diffusion models. SGLang and Meta personnel said the backend is running production inference at Meta, while Tom’s Hardware also reported the open-source release of a matching PyTorch-native vLLM version.

    LMSYS Blog · 🔥 0
  4. Google and Meta Open-Source TorchTPU for PyTorch on TPUs

    Google and Meta’s PyTorch team released TorchTPU as an open-source native backend for running PyTorch workloads on Google Cloud TPUs. It supports standard PyTorch APIs, eager-mode debugging and torch.compile optimization, with integrations spanning Hugging Face Transformers and Diffusers, vLLM, SGLang, llm-d, TorchTitan and LiteRT. Google says its training setup was validated on up to 8,000 Ironwood TPUs and achieved as much as 52% model FLOPS utilization with Qwen3-8B; these figures are vendor claims.

    Hugging Face · 🔥 0
  5. vLLM and SGLang launch native PyTorch backends for Google Cloud TPUs

    vLLM released vLLM TorchTPU, an out-of-tree platform plugin that lets vLLM run on Google Cloud TPUs, and Google said it also launched a native PyTorch backend for SGLang built with RadixArk. Both engines are built on Google's TorchTPU stack, reuse existing model definitions and keep upstream schedulers, KV cache managers and OpenAI-compatible servers, with TPU-specific Pallas kernels for attention, mixture-of-experts and quantized matmuls. Google says the engines are running in production with preview customers and cites SemiAnalysis InferenceX results including 2.01 cents per million tokens, while vLLM lists TPU v7x (Ironwood) as recommended hardware and v6e, v5e and v5p as experimental.

    vLLM Blog · 🔥 0
  6. Google open-sources TorchTPU, giving PyTorch a native TPU backend

    Google announced Tuesday that TorchTPU, its version of PyTorch for TPUs, is now available as an open-source project, adding a native torch.device("tpu") backend so existing PyTorch training scripts need only minimal device-initialization changes. Google and Meta co-developed TPU-native execution passes inside TorchTitan, and a TorchTitan TPU fork is published on GitHub; Google says it worked with Hugging Face, Inferact (vLLM) and RadixArk (SGLang) on tool compatibility. Google reports vendor benchmarks including about 57% MFU for Llama 3 405B from 128 to 1,024 TPU v7x chips and 32.42% MFU for Qwen3 235B on a single 1,024-chip slice, while Amazon released its own PyTorch variant, TorchNeuron, for Trainium and AWS a day earlier.

    Google Developers Blog · 🔥 0

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