vLLM vs SGLang: Choosing an Inference Engine
Choose an inference engine using workload compatibility, reproducibility and operational requirements—not an unqualified leaderboard.
Problem
A throughput result from someone else's GPU and prompt distribution does not tell you which serving engine fits your application.
Conclusion
Start by establishing that each pinned engine supports the exact model and features you need. Then run the same acceptance workload on both. This article defines a comparison method; it does not declare a performance winner.
Environment
Record GPU model and topology, driver, container digest, checkpoint revision, precision, context limit and scheduler settings for each run. Keep hardware and the request set fixed. Document any setting that cannot be matched.
Symptoms
An engine can look fast in a batch test yet fail a latency target at sustained concurrency. Another may load the model but lack a feature your application depends on.
Cause
Serving results depend on request lengths, arrival pattern, cache reuse, quantization, scheduling and measurement boundaries. Aggregate tokens per second hides some user-visible delays.
Solution
Use a small evaluation matrix before performance tuning.
| Gate | What to record |
|---|---|
| Model compatibility | Exact checkpoint, architecture and required features |
| Correctness | Representative prompts and application acceptance criteria |
| Latency | TTFT and TPOT distributions, including tails |
| Throughput | Output tokens and elapsed measurement window |
| Operations | Startup, health checks, cancellation, recovery and observability |
Keep an experiment manifest with the results. This is a schema example, not a completed run.
experiment:
status: not_run
engine: null
engine_version: null
checkpoint_revision: null
gpu_inventory: null
workload_file: null
results_file: null
Warm up each engine, define whether prefix caches are cold or warm, and replay the same requests. Repeat runs to observe variability. Include failures and dropped requests in the report.
Verification
Benchmark data pending real-world testing.
Select an engine only after it passes your feature, correctness, latency and operational gates. Retain raw outputs so another engineer can audit the conclusion.
Caveats
Defaults and feature support change between releases. Do not copy a tuning configuration across engines and assume it expresses the same behavior.