<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Systems on Sandeep Kumar</title><link>https://sandeep007734.github.io/tags/systems/</link><description>Recent content in Systems on Sandeep Kumar</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 07 Aug 2026 00:00:00 +0530</lastBuildDate><atom:link href="https://sandeep007734.github.io/tags/systems/feed.xml" rel="self" type="application/rss+xml"/><item><title>FlashAttention: Exact Attention Without Materialization</title><link>https://sandeep007734.github.io/posts/flashattention/</link><pubDate>Sun, 08 Mar 2026 00:00:00 +0530</pubDate><guid>https://sandeep007734.github.io/posts/flashattention/</guid><description>How FlashAttention avoids materializing quadratic attention matrices by combining tiled execution, online softmax, and kernel fusion.</description></item><item><title>PagedAttention</title><link>https://sandeep007734.github.io/posts/pagedattention/</link><pubDate>Tue, 03 Mar 2026 00:00:00 +0530</pubDate><guid>https://sandeep007734.github.io/posts/pagedattention/</guid><description>How PagedAttention uses virtual-memory-style KV-cache paging to reduce fragmentation and increase LLM serving throughput.</description></item><item><title>The Evolution of FlashAttention: From Ampere to Blackwell</title><link>https://sandeep007734.github.io/posts/flashattention-evolution/</link><pubDate>Fri, 07 Aug 2026 00:00:00 +0530</pubDate><guid>https://sandeep007734.github.io/posts/flashattention-evolution/</guid><description>How FlashAttention-2, -3, and -4 rescheduled the same exact-attention algorithm as GPU bottlenecks shifted from occupancy to asynchronous pipelines and specialized memory.</description></item><item><title>FlashInfer: A Customizable Attention Engine for LLM Serving</title><link>https://sandeep007734.github.io/posts/flashinfer/</link><pubDate>Fri, 07 Aug 2026 00:00:00 +0530</pubDate><guid>https://sandeep007734.github.io/posts/flashinfer/</guid><description>How FlashInfer unifies heterogeneous KV-cache layouts, specialized attention kernels, and dynamic serving schedules in one inference-oriented engine.</description></item></channel></rss>