Breaking Down the vLLM Whitepaper: KV Cache, PagedAttention, and Fast Inference | Research Insights #32 (Series #Inference)

The model fits on the GPU, and the test request finishes quickly. What happens when hundreds of requests with different context and response lengths arrive at once?

On September 28, 2026, at 11:00 Moscow time, in a live stream, I will break down a 2023 whitepaper on vLLM and LLM inference for Research Insights Made Simple #32: how memory management, request scheduling, and state transfer between nodes affect service speed.

We will start with the KV cache—the keys and values of already processed tokens that the model retains for later generation steps. We will see how PagedAttention applies virtual memory ideas to this cache and allows active requests to be placed more efficiently on a GPU. Then we will look at the problems that have to be solved around the engine.

This episode is for engineers, architects, and platform teams who deploy LLMs, choose an inference engine, or want to understand what happens behind a model API.

PagedAttention paper, SOSP 2023: "Efficient Memory Management for Large Language Model Serving with PagedAttention" All Research Insights Made Simple episodes are available on the website.

Tell us in the comments what limits your inference: memory, time to first token, pauses during generation, or serving cost.

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