Inside vLLM: Anatomy of a High-Throughput LLM Inference System (modal.com)

The notebook walks through vLLM’s runtime internals, unpacking how a high-throughput LLM inference engine is constructed and executed. It inspects the LLM object and its llm_engine, vllm_config, processor (input validation/tokenization), output_processor, and the engine_core. The engine_core contains a model_executor (UniProcExecutor → MultiProcExecutor for multi‑GPU), scheduler (FCFS or priority queues), KV cache manager (paged attention), and a structured output manager for guided decoding. Worker initialization sets device, checks dtype/VRAM vs. gpu_memory_utilization, sets distributed modes (DP/TP/PP/EP), and instantiates a model_runner and CPU-side InputBatch. The generate flow is driven by repeated engine.step() calls that run scheduling, forward passes, and output conversion; forward execution delegates from executor → worker → model_runner → PyTorch. Technically significant details include vLLM’s block‑aligned, paged KV cache (avoids recomputing incomplete blocks), scheduler policies for batching/priority, and the clear separation between request processing, execution, and output stages—design choices that enable low-latency, high-throughput serving. The notebook highlights practical knobs (memory utilization, sampling params, multi‑process executor) and shows how caching, careful buffer management, and per-worker setup enable scalable multi‑GPU deployments. For engineers and researchers, it’s a concise map of the tradeoffs and mechanisms to optimize inference throughput, latency, and memory use in production LLM systems.