Comparison
vLLM vs SGLang
Compare vLLM and SGLang for high-throughput open model serving, modern MoE support, structured generation, and deployment complexity.
Quick verdict
Use vLLM as a mature serving baseline. Test SGLang for newer model support and structured generation workflows.
Choose which
Choose vLLM when throughput and broad serving adoption matter.
Choose SGLang when it supports your exact model and serving pattern well.
Feature table
Serving maturityStrongFast-moving
Structured generationGoodStrong
Best userInfra teamInfra/research team
Recommendation
Benchmark both on your exact model, quantization, context length, and traffic pattern before choosing.
Setup difficulty
Both are advanced.
Best use cases
- GPU model serving
- OpenAI-compatible APIs
- High-throughput inference
Limitations
- Both require GPU infrastructure and model-specific testing
Related links
FAQ
Can I choose based on generic benchmarks?
Use benchmarks as a clue, not a decision. Your model and traffic pattern matter more.
Sources
Keep building your stack
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