Production LLM Deployment: vLLM & KV Caching

Achieve 10x higher serving throughput using PagedAttention, KV Cache optimization, continuous batching, and TensorRT-LLM.

1. The KV Cache Bottleneck in Autoregressive Generation

During generation, token \(t\) requires attention keys \(K\) and values \(V\) from all previous tokens \(1 \dots t-1\). Recomputing them at each step is \(O(T^2)\). The **KV Cache** stores these matrices in GPU VRAM.

\[ \text{Memory}_{\text{KV}} = 2 \times 2 \times n_{\text{layers}} \times n_{\text{heads}} \times d_{\text{head}} \times b \times s \quad \text{(bytes)} \]

2. PagedAttention & vLLM (Kwon et al., SOSP 2023)

Standard contiguous VRAM allocation wastes 60-80% of memory due to fragmentation. PagedAttention partitions the KV cache into non-contiguous virtual memory blocks (pages), increasing batch concurrency and serving throughput by **up to 24x**!

🎯 Module Mastery Certification Quiz

+100 XP

How does PagedAttention (vLLM) dramatically increase LLM serving throughput?

By managing KV cache in non-contiguous virtual memory pages like an OS page table, eliminating internal memory fragmentation and allowing massive batch sizes.

By deleting 50% of the transformer layers.

By replacing attention with simple regex rules.

By disabling GPU floating point calculations.