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Don't just focus on vLLM, SGLang is better in complex prompt word scenarios

Written by
Caleb Hayes
Updated on:June-13th-2025
Recommendation

SGLang and vLLM compete in large model reasoning optimization. SGLang is superior in complex prompt word scenarios.

Core content:
1. Comparison of the core goals and applicable scenarios of SGLang and vLLM
2. Analysis of the differences between the two in key technologies and performance
3. Comparison of ease of use and ecology, as well as recommendations for actual application scenarios

Yang Fangxian
Founder of 53A/Most Valuable Expert of Tencent Cloud (TVP)

As frameworks focusing on large model reasoning optimization, SGLang  and  vLLM  are both popular choices for current high-performance reasoning, but they differ significantly in design goals, optimization focus, and applicable scenarios. The following is a detailed comparison:

1. Core objectives and positioning

frameCore Goals
Applicable scenarios
vLLMMaximize throughput & high concurrency
High-traffic API services and batch reasoning
SGLangOptimize complex prompts & structured generation delay
Agent, reasoning chain, JSON generation and other interactive scenarios

2. Comparison of key technologies

technologyvLLMSGLang
Memory optimizationPagedAttention
 (Video memory paging management)
RadixAttention
 (prefix shared tree)
Prompt processing
Standard Attention Mechanism
Runtime prompt word compilation
 (Automatically merge similar prefixes)
Decoding optimization
Conventional incremental decoding
Nested Tensor Parallelism
 + State reuse
Structured output
Requires external library assistance
Native support for JSON/Regex and other constraint decoding

3. Performance characteristics

  • vLLM Advantages :

    • Throughput King : Under concurrent requests (such as >100 QPS), the throughput can reach  10-24 times that of HuggingFace Transformers .

    • The video memory utilization rate is extremely high and can carry longer contexts (such as 1M tokens).

    • ☁️ Cloud service friendly: supports dynamic expansion and contraction.

  • SGLang Advantages :

    • ⚡  Low-latency structured generation : 3-5 times faster than vLLM in Agent scenarios (multi-step reasoning + JSON output)  .

    • Complex prompt optimization : For System Prompt + Few-shot scenarios, pre-compiled prompt words can speed up  by 2-3 times .

    • Native support for parallel function calls (such as parallel calls to a search engine + calculator).

4. Usability and Ecosystem

DimensionsvLLMSGLang
API Compatibility
✅ OpenAI API protocol compatible
❌ Independent API design
Deployment complexity
Simple (direct replacement for HF models)
Need to adapt to SGLang runtime
Debugging support
Standard log
Visualize execution trace

5. How to choose?

Demand ScenarioRecommended Solution
Highly concurrent API services
✅  vLLM
Batch Summarization/Translation
✅  vLLM
AI Agent/ReAct Reasoning Chain
✅  SGLang
Strong structured output (JSON/Regex)
✅  SGLang
Low latency interactive applications
✅  SGLang
Very long context (>100K tokens)
✅  vLLM

Summarize

  • vLLM = Nginx for reasoning : suitable for building high-throughput, high-concurrency production-level services.

  • SGLang = Structured Generation Accelerator : Designed for complex prompt words and constraint decoding, greatly improving the efficiency of Agent-type tasks.

Innovative solution : The two can be used together! Use SGLang to handle complex prompt preprocessing, and use vLLM for distributed reasoning. The combined delay is reduced by 40%+

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