In high-scale enterprise AI deployments, inference compute accounts for over 85% of total hardware lifecycle costs. Optimizing token throughput per second per dollar is an engineering necessity. The two leading open acceleration engines—vLLM and NVIDIA TensorRT-LLM—are redefining inference economics.

PagedAttention & Virtual Memory Management

The core bottleneck in transformer inference is the quadratic KV cache memory footprint. vLLM solves this by implementing PagedAttention: - Inspired by virtual memory and paging in OS kernels. - Stores KV cache entries in non-contiguous physical memory blocks. - Eliminates internal fragmentation, enabling up to 4x larger batch sizes on identical GPU clusters.

Continuous Batching vs Static Scheduling

Traditional inference engines batch requests statically, forcing all sequences to wait until the longest response completes. Continuous batching dynamically schedules new incoming prompt tokens at each iteration cycle, maintaining 98%+ GPU compute tensor core utilization.