vLLM vs SGLang: PagedAttention KV Cache Benchmarks on NVIDIA H200

Both vLLM and SGLang have redefined large language model serving performance over the past year, becoming standard choices across NovaPod's one-click template library.

In this technical benchmark, we test both engines on single-device NVIDIA H200 SXM5 instances (141 GB HBM3e) across 1,000 synthetic multi-turn conversations featuring heavy system prompt reuse and structured JSON outputs.

SGLang's RadixAttention algorithm demonstrated a 34% lower Time to First Token (TTFT) when processing repeated agent context headers and tool descriptions.

Meanwhile, vLLM's Chunked Prefill delivered higher throughput stability under bursty concurrent user loads exceeding 128 requests. Both stacks are fully supported with one-click deployment in the NovaPod Marketplace.