Mask the magnitude, look it up, shift the sign bit into position, OR them together — four operations, no branching, no blending.
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收集足够机器人数据需要万台机器人持续运行,这在制造与部署上都是巨大挑战。因此特斯拉计划先部署机器人收集数据。除工业流水线外,机器人广泛应用要等到2028年后。。7zip下载对此有专业解读
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When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention improves this by breaking the KV cache into smaller, flexible chunks that are allocated only when needed, similar to how virtual memory works. It also allows multiple requests with the same starting prompt to share memory and only duplicate it when their outputs start to differ. This approach greatly improves memory efficiency, allowing significantly higher throughput with very little overhead.