Geek Out Time: Demystifying vLLM’s KV Cache, Latency & Context Isolation for Faster LLMs

In the earlier blog ( https://medium.com/the-constellar-digital-technology-blog/geek-out-time-llm-inference-exposed-how-it-works-and-how-to-make-it-fast-on-google-colab-6cdcdbfc390b ), we cracked open how LLM inference works under the hood. That was a good start — but now it’s time to go deeper.

Enter vLLM ( https://github.com/vllm-project/vllm ): a fast, open-source, production-grade inference engine designed for serving large language models efficiently. Its secret weapon? PagedAttention and smart KV cache reuse, both promising big latency wins.

In this follow-up, we’ll run a few hands-on experiments in Google Colab to:

What is vLLM?

This is an excerpt — the full article continues on Medium.