GPU memory is often the bottleneck in large language model inference, yet many engineers lack a clear mental model of where memory actually goes. This deep dive breaks down the problem into three layers: the memory wall that limits single-GPU capacity, distributed parallel strategies that trade communication for memory, and the PagedAttention mechanism that eliminates fragmentation in KV cache management.
vLLM's PagedAttention is particularly noteworthy because it borrows virtual memory paging concepts from operating systems, allowing attention KV caches to be stored in non-contiguous memory blocks. This reduces waste by up to 90% in some workloads and enables higher batch sizes, directly improving throughput and cost efficiency.
For teams running production LLM services, understanding these mechanisms is not optional. It informs decisions on GPU selection, parallelism configuration, and serving framework choice. As models grow, the gap between memory demand and hardware supply will only widen, making such knowledge increasingly valuable.