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@PyTorch

Our latest community blog explores how sparsity can unlock new efficiency for LLM inference in PyTorch. As model sizes and compute needs grow, sparsity offers a clear path to faster, more energy-efficient deployment. In this post, Kira Selby and Varun Khare (@AKAvkkhare), @NimbleEdgeInc, share techniques like CETT thresholding, Relufication, weight caching, and statistical top-k that enable up to 6x faster inference and advance a unified framework for sparse inference. šŸ”— Read the blog: https://t.co/fzt5KLTK5x #PyTorch #AIInfrastructure #EdgeAI #OpenSource

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