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The Ray project is forming a vendor-neutral working group to make its support for hardware accelerators (GPUs, TPUs, Trainium, Gaudi, and others) easier to use, more efficient at scale, and more reliable in production. The announcement is a roadmap with three named areas and no shipped features yet, so the practical impact depends on what the group delivers. For AI engineers, the key question is whether Ray's accelerator abstraction reduces lock-in and operational friction across fragmented hardware.

Sources coveringPyTorch, InfoQ, arXiv, 36Kr

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Announcing the Special Interest Group (SIG) for Ray on Accelerators

PyTorchNovember 2, 20266 min read

Today, we are announcing the Special Interest Group (SIG) for Ray on hardware accelerators. When Ray was created in UC Berkeley over 10 years ago, the computing landscape looked very different. At the time, “distributed computing” referred to CPU clusters running frameworks like Apache Hadoop and Apache Spark. GPUs were only just moving from graphics and scientific computing into machine learning. Today, accelerated computing is front and center in the development and deployment of AI, and accelerators and their capabilities have become more specialized and diverse. For example: NVIDIA HGX B200 and the full-stack platform provide substantially greater memory capacity to support larger models, and NVIDIA GB200 NVL72 connects 72 NVIDIA Blackwell GPUs for communication-intensive, large-scale training…

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