Capability · Comparison
SGLang vs vLLM
vLLM kicked off the modern era of open-source LLM inference with PagedAttention and continuous batching; it's the default serving engine for most open-weights deployments. SGLang arrived later with aggressive kernel optimisations, strong MoE support, and a distinct frontend language for structured generation. As of 2026-04, SGLang has caught up — and on specific workloads (DeepSeek MoE, structured outputs, speculative decoding) it leads.
Side-by-side
| Criterion | SGLang | vLLM |
|---|---|---|
| License | Apache 2.0 | Apache 2.0 |
| Maintainer | SGL team (LMSYS-adjacent) | vLLM team (UC Berkeley origin, now community + commercial backers) |
| Model coverage | Broad — Llama, Qwen, DeepSeek, Mistral, Phi, Gemma | Broadest — dozens of architectures including experimental |
| MoE throughput (e.g., DeepSeek V3) | Best-in-class | Good, catching up |
| Structured generation speed | Very fast — native RadixAttention for prefix sharing | Good via Outlines or XGrammar integrations |
| Speculative decoding | Native, multiple algorithms supported | Native, including medusa-style |
| Quantization formats | GPTQ, AWQ, FP8, INT4 | GPTQ, AWQ, FP8, INT4, Marlin, Bitsandbytes |
| Distributed serving | Tensor parallel + DP | Tensor parallel, pipeline parallel, DP |
| Ecosystem maturity | Growing fast | Most mature — docs, tutorials, helm charts, integrations |
Verdict
vLLM remains the default choice for most teams in 2026 because of its ecosystem: broadest model support, deepest documentation, and the most battle-tested Kubernetes deployments. SGLang is the winner for specific workloads — if you're serving DeepSeek V3 MoE, running heavy structured-generation pipelines, or hitting the limits of vLLM's throughput on large-batch inference, SGLang is often 10-30% faster end-to-end. Benchmark on your actual model and load before committing; both are fast-moving and the gap narrows every release.
When to choose each
Choose SGLang if…
- You're serving DeepSeek V3 or another large MoE model.
- Your pipeline is heavy on structured generation (JSON, regex).
- Prefix sharing across many similar prompts matters (RadixAttention).
- You're chasing the last 20% of throughput on specific workloads.
Choose vLLM if…
- You want the broadest model support and most mature ecosystem.
- You're new to LLM serving and want the most docs / examples.
- Your team knows vLLM already — switching cost isn't worth it.
- You need integrations (Helm, Ray, KServe) that are further along for vLLM.
Frequently asked questions
Can I switch between vLLM and SGLang without changing my client code?
Mostly yes — both expose OpenAI-compatible HTTP endpoints. The client stays identical; you swap the server. Some advanced features (tool-use semantics, structured output control flags) differ slightly.
Do these support multi-GPU inference?
Yes. Both support tensor parallelism (splitting weights across GPUs) and data parallelism (replicating the model). vLLM also supports pipeline parallelism which matters for very large models. For production at scale, pair either with Ray or Kubernetes.
Which is better for running open-weights models in production today?
For most teams: vLLM. For MoE-heavy or structured-generation-heavy workloads: SGLang. Run both on your target model with realistic traffic patterns before committing — the gap moves release by release.
Sources
- SGLang — GitHub — accessed 2026-04-20
- vLLM — Docs — accessed 2026-04-20