Inside vLLM PagedAttention & Chunked Prefill Engine
An evidence-audited, 20-diagram interactive system breakdown tracing vLLM BlockAllocator virtual KV cache memory block management, PagedAttention CUDA kernel non-contiguous VRAM lookup, Chunked Prefill prompt co-scheduling, CUDA Graph decode execution, and Grouped-Query Attention (GQA) memory bandwidth optimization.
Executive Summary
vLLM has revolutionized high-throughput LLM serving by introducing PagedAttention—a virtual memory management architecture for Key-Value (KV) caches inspired by operating system virtual memory page tables. This 20-diagram interactive system breakdown deconstructs vLLM's internal scheduling and CUDA execution engine across 5 specialized chapters:
1. vLLM BlockAllocator & Virtual Page Tables
Traditional LLM serving engines allocate contiguous GPU memory blocks for the maximum context length of every request, wasting up to 60-80% of VRAM due to internal and external memory fragmentation. vLLM's BlockAllocator partitions GPU VRAM into fixed-size physical memory blocks (typically 16 tokens). Sequences allocate logical token blocks that map dynamically to physical VRAM blocks via block tables.
2. PagedAttention CUDA Kernel Execution
Because physical memory blocks are non-contiguous in VRAM, standard FlashAttention or cuDNN kernels cannot compute dot-product attention directly. vLLM's PagedAttention CUDA kernel accepts a block table array, fetching key-value vectors from non-contiguous VRAM memory blocks on-the-fly during warp-level execution.
3. Chunked Prefill & Prompt Co-Scheduling
Prompt execution consists of two distinct phases: Prefill (compute-bound parallel processing of prompt tokens) and Decode (memory-bandwidth-bound sequential token generation). vLLM's Chunked Prefill scheduler splits long prompt prefill sequences into fixed-size token chunks (e.g. 512 tokens), co-scheduling prefill chunks alongside decode tokens in the same iteration to maintain near 100% GPU compute saturation.
4. CUDA Graph Decode Execution Runner
During the token generation decode phase, launching individual CUDA kernels for every transformer layer introduces significant CPU-side Python overhead. vLLM captures the static decode execution DAG into CUDA Graphs (cudaGraphInstantiate), allowing the GPU to replay the entire decode forward pass with a single C-level launch call.
5. Grouped-Query Attention (GQA) & KV Cache Optimization
To further reduce VRAM bandwidth requirements during multi-token generation, modern LLMs (like Llama 3 and Mistral) utilize Grouped-Query Attention (GQA). vLLM's KV cache manager shares key-value heads across multiple query heads, cutting KV cache VRAM footprint by 4x to 8x and enabling up to 8x larger batch sizes.