1. The KV Cache Bottleneck in LLM Serving
Serving Large Language Models (LLMs) in production is memory-bound rather than compute-bound. During token generation, the model caches Key and Value (KV) activation vectors for all preceding tokens in GPU VRAM to avoid redundant recomputations. In traditional inference servers (Hugging Face Transformers), memory is allocated contiguously for the maximum possible sequence length, resulting in 60% to 80% of expensive GPU VRAM sitting wasted due to memory fragmentation.
2. PagedAttention: Virtual Memory for LLMs
Developed by UC Berkeley researchers, PagedAttention resolves memory fragmentation by applying the classic operating system concept of virtual memory and paging to the KV cache. Non-contiguous physical memory blocks are dynamically mapped to logical token pages, allowing the KV cache to grow on-demand without pre-allocation waste.
3. Throughput Benchmarks and Continuous Batching
vLLM combines PagedAttention with Continuous Batching (iteration-level scheduling). Instead of waiting for an entire batch of requests to finish generating before accepting new prompts, vLLM dynamically injects new requests into the running batch on every iteration step. This achieves up to 24x higher throughput compared to standard Hugging Face pipelines.
4. Production Deployment on Triton and Kubernetes
Deploy vLLM clusters on Kubernetes using NVIDIA GPU operator integrations. Exposing standard OpenAI-compatible REST API endpoints (/v1/chat/completions) allows engineering teams to point internal applications to self-hosted vLLM clusters with zero client-side code alterations.
