SGLang arrived as a vLLM challenger promising better structured output and comparable throughput. By 2026 it has matured enough to take seriously in production. On identical dedicated GPU hardware we have benchmarked both. Here is what differs.
Contents
Overview
vLLM: general-purpose high-throughput LLM serving. PagedAttention, continuous batching, OpenAI-compatible API. Mature, widely deployed, broad model support.
SGLang: high-throughput plus a DSL (SGLang Program) for structured generation, constrained decoding, and agent workflows. Radix attention for prefix sharing.
Throughput
For unstructured chat throughput on Llama 3 8B on a single 5090:
| Engine | Tokens/sec (batch 64) |
|---|---|
| vLLM | ~1,800 |
| SGLang | ~1,900 |
Roughly tied. SGLang’s Radix attention wins on workloads with heavy shared prefixes (agent chains, few-shot prompts) – there it can be 2-3x faster. For isolated requests the engines are within noise.
Unique Features
SGLang advantages: native structured output via regex or JSON schema with better performance than vLLM’s guided decoding. First-class agent chaining. Radix attention for deduplication of shared prompt prefixes.
vLLM advantages: broader model support. More deployments in production means more community troubleshooting. Easier integration with observability stacks. More frequent releases.
Either Engine Preconfigured
We set up vLLM or SGLang on your dedicated server with your target model.
Browse GPU ServersWhen to Pick Which
Pick SGLang when:
- Structured output (JSON, regex-constrained) is central to your workload
- You have many requests sharing common prefixes (agent systems, few-shot prompts)
- You are building agent chains and want native support
Pick vLLM when:
- You want the best-supported, most-deployed option
- Your workload is unstructured chat or generic completion
- You need a specific model that vLLM supports but SGLang might not
Both work well. Neither is wrong in 2026. See vLLM tuning and vLLM structured output.
