Understanding LLM Inference Engines: Inside Nano-vLLM (Part 1)
Summary
The article provides a deep dive into Nano-vLLM's inference-engine architecture, describing how prompts are turned into sequences, batched, and scheduled to optimize throughput while managing GPU resources. It covers the producer-consumer flow, prefill vs decode phases, the Block Manager's KV cache control plane, and tensor parallelism with CUDA-graphs and sampling. These insights help engineers design production-ready LLM inference pipelines for enterprise-scale workloads.