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eric
2025-12-27 22:05:57 +00:00
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<!doctype html><html lang=en><head><title>vAttention · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com; img-src 'self'; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:*;"><meta name=author content="Eric X. Liu"><meta name=description content="Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While PagedAttention (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.
<!doctype html><html lang=en><head><title>vAttention · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com; img-src 'self'; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:*;"><meta name=author content="Eric X. Liu"><meta name=description content="Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While PagedAttention (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.
The Status Quo: PagedAttention and Software Tables
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Eric X. Liu
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