How vLLM Solves GPU Memory Bottlenecks in Large Language Model Inference

Modern LLM text generation is primarily a memory bandwidth problem, not a compute problem, with GPUs often running at low utilization while shuffling model weights and KV cache data during the decode phase. Standard PyTorch inference pipelines suffer from memory fragmentation, static batching inefficiencies, and high driver overhead when handling multiple concurrent users. vLLM addresses these limitations through two core innovations: PagedAttention, which manages KV cache memory more efficiently, and continuous iteration-level batching, which schedules new requests as soon as any sequence completes. Benchmarks for the article were conducted on dedicated NVIDIA H100 and H200 GPU clusters, using models such as Qwen3.6-27B as practical examples. The piece provides a technical deep dive into why naive inference stacks fail at scale and how vLLM's operating-systems-inspired engineering overcomes those constraints.