SGLang & the Benchmark
Two engines, one methodology, publishable numbers.
SGLang gets equal billing because job postings list it as vLLM's equal, and because RadixAttention is a genuinely different idea: the KV cache as a prefix tree shared across requests, which turns multi-turn and agentic traffic (everyone's traffic now) into a cache-hit problem. The methodology reading sits before the head-to-head on purpose. Most published LLM benchmarks are subtly wrong, usually through unrealistic length distributions or a single conflated latency number, and the goal of this quest is to make you incapable of producing one.
Report the head-to-head as Pareto curves with TTFT and ITL separated, and goodput at a stated SLO. A bare tokens/sec claim with no latency constraint is the field's most common lie. That framing carries straight into the observability phase, and the published post at the end is an artifact a laptop-bound candidate can't fake: your numbers, your hardware, reproducible configs.
- Inference EngineeringPhilip Kielybook↗
Prefix-tree KV reuse. SGLang is co-equal with vLLM in job postings — know both.
TTFT, ITL/TPOT, goodput, and the measurement pitfalls. Realistic length distributions or it doesn't count.
Latency-throughput Pareto curves, TTFT/ITL separated, goodput at a stated SLO, cost/M tokens.
bench+200 XPInferQuest streams real completions against your endpoint and takes medians of 3 runs: TTFT ≤ 800ms and ≥ 25 tok/s single-stream. Tune until it passes.
bench+250 XPauto-verifiedThe post no laptop-bound candidate can write — real hardware, real methodology, reproducible configs.
write+200 XPauto-verified