Inside vLLM: anatomy of a high-throughput LLM inference system
The strongest end-to-end explanation of request processing, continuous batching, PagedAttention, prefix caching, scheduling, decoding, and throughput trade-offs.
vLLM · Learning path
How does vLLM serve a language model?
A path through prefill, decode, continuous batching, scheduling, key-value cache management, PagedAttention, and next-token generation.
Engineers who deploy language models and want to explain the work performed between an HTTP request and a streamed token.
- 3
- stages
- 3
- selected readings
- 01
Stage 1
Understand the complete inference loop
Distinguish prefill from decode and connect scheduling, memory allocation, forward passes, sampling, batching, and streaming.
- 02
Stage 2
Trace the request across processes
Follow tokenization, engine scheduling, key-value cache allocation, worker execution, output processing, and streaming through vLLM V1.
vLLM architecture overview
Maps the API server, engine core, scheduler, cache manager, and GPU workers onto the current process architecture and execution path.
- 03
Stage 3
Understand the memory design behind batching
Explain why the key-value cache limits batch size and how paging reduces fragmentation while allowing blocks to be shared.
Efficient memory management for large language model serving with PagedAttention
Introduces the memory problem, PagedAttention, cache sharing, scheduling consequences, and the evaluation behind the original vLLM system.