vLLM vs SGLang: Choosing an Inference Engine
Choose an inference engine using workload compatibility, reproducibility and operational requirements—not an unqualified leaderboard.
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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
This article provides a verification procedure; no hardware measurements are claimed.
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.
Turn requirements into a decision
Start with three representative request groups: short interactive questions, long-document questions and output-heavy generation. Assign each the latency and correctness criteria your application actually needs. A single aggregate score cannot represent all three.
| Decision point | Evidence to collect | If the gate fails |
|---|---|---|
| Required output format | Parse outputs from your acceptance prompts | Fix compatibility before tuning speed |
| Interactive latency | TTFT distribution at expected arrival rate | Inspect queueing and scheduling |
| Long-document handling | Correctness and memory at target context | Reduce scope or choose a supported configuration |
| Operational recovery | Startup, cancellation and a controlled restart | Document the recovery gap |
The comparison need not end with a universal winner. One engine may fit a specific feature set while another fits your deployment tooling. Record those tradeoffs explicitly so a future release can be evaluated against the same requirements.
Avoid an attractive but incomplete score
Imagine run A completes 90 of 100 requests in 60 seconds, while run B completes all 100 in 65 seconds. Comparing only tokens from successful responses hides run A's failures. First apply your reliability gate; then compare latency and throughput among configurations that pass. These numbers illustrate reporting choices, not measured engine performance.
Keep a small bundle for every run: a manifest, workload checksum, launch command, request-level results and a short conclusion. In request-level results include status, input/output token counts and timing boundaries. Report repeated runs individually before aggregating them. If precision or cache settings cannot be matched, label the comparison as a system configuration comparison rather than isolating an engine effect.
Download the blank recording sheet and read the benchmark protocol.
References
Revision note
September 14, 2026: added a worked decision framework and explicit verification limits. Original publication date retained.