vLLM Inference System Anatomy Detailed in New Technical Series
A new blog post begins a series breaking down the architecture of vLLM, a high-throughput LLM inference system. The first installment covers core components like scheduling, paged attention, and continuous batching, plus advanced features such as chunked prefill and prefix caching. Future posts will explore subsystems, scaling, serving, and benchmarking.
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In this post, I'll gradually introduce all of the core system components and advanced features that make up a modern high-throughput LLM inference system. In particular I'll be doing a breakdown of how vLLM [[1]](https://www.aleksagordic.com/blog/vllm#ref-1) works. This post is the first in a series. It starts broad and then layers in detail (following an inverse-pyramid approach) so you can form an accurate high-level mental model of the complete system without drowning in minutiae. Later posts will dive into specific subsystems. This post is structured into five parts: 1. LLM engine & engine core: fundamentals of vLLM (scheduling, paged attention, continuous batching, etc.) 2. Advanced features: chunked prefill, prefix caching, guided & speculative decoding, disaggregated P/D 3. Scaling up: from single-GPU to multi-GPU execution 4. Serving layer: distributed / concurrent web scaffolding 5. Benchmarks and auto-tuning: measuring latency and throughput 📝Notes * Analysis is based on com