Inside vLLM: A 41-Minute Anatomy of a High-Throughput Inference System
techNmak · x · 2026-10-17
Entry 6 in an LLM inference learning list: Aleksa Gordic's long-form Inside vLLM (on the vLLM blog). Based on an August 2025 vLLM V1 snapshot, it systematically breaks down every core component of a modern high-throughput inference system:
- LLM Engine & Engine Core: scheduling, paged attention, continuous batching
- Advanced features: chunked prefill, prefix caching, guided & speculative decoding, disaggregated P/D
- Scaling from single-GPU to multi-GPU, multi-node execution
- Distributed/concurrent web serving layer
- Benchmarks and auto-tuning for latency and throughput
Ideal for anyone wanting to understand or contribute to vLLM, SGLang, and similar engines.
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