Research Insights #32 Materials: vLLM and PagedAttention (Category #AI)
I have collected the materials for the solo Research Insights Made Simple episode from September 28, 2026, about vLLM and PagedAttention. It is the story of how the operating-systems idea of paged memory helped serve LLM requests more efficiently. The episode examined the September 2023 paper "Efficient Memory Management for Large Language Model Serving with PagedAttention."
The model already fits on the GPU, but its weights must share memory with the KV cache—the keys and values of processed tokens for every active request. As the response grows, the cache grows with it, while the final response length is not known in advance. Reserving memory with a margin leaves progressively less room for neighboring requests. This is where an operating-systems textbook came in handy.
In the episode, I covered:
🔸 Where the memory goes Reservations for future tokens, unused regions, and fragmentation—and why even knowing the response length does not solve every placement problem.
🔸 How PagedAttention works A table maps a request’s logical blocks to physical GPU blocks, and new blocks are allocated as needed.
🔸 How to share a common prefix Several response variants reuse the same KV data, while changing a shared block triggers copy-on-write.
🔸 What to do when memory still runs out Move the state to CPU memory or discard it and recompute it later: each option has its own cost.
🔸 Why a slower kernel can speed up the whole service Saving memory makes it possible to process more requests concurrently; the result must be measured together with latency.
In the 2023 paper, the authors reported 2–4× higher throughput than FasterTransformer and Orca at comparable latency. This was the result of their experiments with specific models and workloads. The gain would have to be measured again for a current server.
Episode materials: 📌 Episode page with timestamps 📊 Slides 🎬 YouTube, VK Video 🎧 Podster, Yandex Music, Apple Podcasts 📝 Text summary
If you have already tuned vLLM for your workload, tell us in the comments what bottleneck you hit and what produced a noticeable gain.
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