# PyTorch 2.13 Adds FlexAttention for Apple Silicon: 12x Speedup on Sparse Attention

> PyTorch 2.13 brings FlexAttention to Apple Silicon, and the number attached is eye-catching: up to a 12x speedup on sparse attention patterns. Attention is the core operation inside modern AI models and also one of the most expensive, so making it dramatically faster on Apple's M-series chips matters for anyone who wants to run capable models on a laptop instead of renting a cloud GPU. Sparse attention is the trick being accelerated. Instead of having every token attend to every other token, which grows costly as inputs get longer, sparse patterns let a model skip the connections that do not matter. FlexAttention gives developers a flexible way to express those patterns and, now, to run them efficiently on consumer hardware. The quiet significance is on-device AI. Every gain that lets a strong model run locally chips away at the assumption that serious inference has to live in a data center, with implications for cost, privacy and who controls the compute. It is a plumbing update, not a headline model, but this is the kind of change that decides where AI actually runs.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: AIapps · Published Thursday, July 16, 2026_

## Wortins' read

PyTorch 2.13 brings FlexAttention to Apple Silicon, and the number attached is eye-catching: up to a 12x speedup on sparse attention patterns. Attention is the core operation inside modern AI models and also one of the most expensive, so making it dramatically faster on Apple's M-series chips matters for anyone who wants to run capable models on a laptop instead of renting a cloud GPU. Sparse attention is the trick being accelerated. Instead of having every token attend to every other token, which grows costly as inputs get longer, sparse patterns let a model skip the connections that do not matter. FlexAttention gives developers a flexible way to express those patterns and, now, to run them efficiently on consumer hardware. The quiet significance is on-device AI. Every gain that lets a strong model run locally chips away at the assumption that serious inference has to live in a data center, with implications for cost, privacy and who controls the compute. It is a plumbing update, not a headline model, but this is the kind of change that decides where AI actually runs.

## Source

[Read the full story at AIapps](https://www.aiapps.com/blog/july-ai-mega-update-major-breakthroughs-launches/)

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