If you deploy a foundation model using naive PyTorch inference scripts, your server will struggle to handle more than a handful of concurrent users. High-throughput serving engines like vLLM and SGLang represent masterpieces of systems engineering that unlock maximum hardware utilization from modern GPUs.
The Straggler Problem in Static Batching
Traditional deep learning batching processes requests in synchronized groups. If Request A generates a short 10-token answer while Request B generates an extensive 1,000-token analysis, Request A's GPU allocation sits completely idle, locked in memory until Request B finishes.
[Static Batching: GPU Wasted on Stragglers]
Batch 1: [Req A: 10 tokens (Finished)] ──► IDLE IDLE IDLE IDLE (Wasted Compute!)
[Req B: 1000 tokens (Running...)]
[Continuous Iteration-Level Batching: Dynamic Slot Filling]
Step 10: [Req A Finishes] ──► [Req C Enters Batch Instantly!]
[Req B Continues...]
Continuous Batching: Iteration-Level Scheduling
Continuous batching solves this by operating at the iteration level rather than the request level. After every individual token generation step across the GPU, finished requests are immediately evicted, and incoming requests from the queue take their place instantly without waiting for long-running jobs to conclude.
PagedAttention: Virtual Memory for KV Caches
Prior to PagedAttention, serving engines had to pre-allocate contiguous GPU memory for the maximum possible sequence length (e.g. reserving 8,192 tokens of memory per request). In practice, because requests had variable lengths, over 60% of GPU memory was wasted on internal and external fragmentation.
PagedAttention solved this by implementing the virtual memory paging principles of modern operating systems: KV caches are divided into fixed-size physical blocks (e.g. 16 tokens per page) and allocated non-contiguously on demand, increasing GPU serving throughput by up to 4x at zero accuracy loss.