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summary
Meta announces a PyTorch stack with concrete tools and partnerships: Helium lets engineers write GPU kernels in ~30 lines with autotuning and better-than-Triton performance; Monarch provides a single-controller distributed orchestration designed to replace ad hoc PyTorch distributed setups; Executor 1.0 reaches GA as an on-device end-to-end runtime used at scale with Qualcomm, ARM and Cadence partners. Six deep-dive blog posts drop today and there’s a new collectives library and an RL-focused Forge on top of the stack.
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