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SGLang vs. vLLM：两大主流大模型推理引擎深度对比与选型指南

2026/2/3
SGLang vs. vLLM：两大主流大模型推理引擎深度对比与选型指南
AI Summary (BLUF)

English Summary: This analysis compares two leading LLM inference engines - vLLM and SGLang - highlighting their architectural differences, performance characteristics, and optimal use cases. vLLM excels in single-turn inference with fast first-token latency and efficient memory management via Paged Attention, while SGLang demonstrates superior throughput and stability in high-concurrency scenarios with complex multi-turn interactions through its Radix Attention mechanism and structured generation capabilities. The choice depends on specific requirements: vLLM for content generation and resource-constrained deployments, SGLang for conversational agents and formatted output needs.

中文摘要翻译：本文深度对比两大主流大模型推理引擎vLLM和SGLang，解析其架构差异、性能表现和适用场景。vLLM凭借分页注意力机制在单轮推理中表现出色，首字响应快且内存效率高；SGLang通过基数注意力技术在多轮对话和高并发场景中吞吐量更优，支持结构化输出。选择建议：内容生成等单轮任务选vLLM，复杂对话和格式输出需求选SGLang。

引言

在部署大型语言模型（LLM）时，选择合适的推理框架对于实现高性能、高吞吐和低延迟至关重要。目前，SGLang和vLLM是两个备受关注的流行框架，它们的设计哲学和优化侧重点各有不同。本文旨在对这两个框架进行深度技术剖析，通过对比其核心架构、关键技术以及性能表现，为开发者和架构师提供一份清晰的选型指南。

在部署大型语言模型时，选择合适的推理框架是实现高性能、高吞吐和低延迟的关键。SGLang和vLLM是目前两个主流的框架，它们的设计理念和优化重点各有侧重。本文将对两者进行深入的技术分析，对比其核心架构、关键技术和性能表现，为开发者和技术决策者提供一份实用的选型参考。

核心概念解析

什么是 SGLang？

SGLang，全称 Structured Generation Language（结构化生成语言），是一个专为复杂LLM程序执行而设计的推理框架。它旨在解决大模型部署中的痛点，通过优化CPU和GPU的协同工作，实现更高的系统吞吐量。其核心思想是最大限度地减少重复计算，并以相对简单的方式赋能LLM应用开发。

SGLang, which stands for Structured Generation Language, is an inference framework designed for executing complex LLM programs. It aims to address the pain points in large model deployment by optimizing CPU and GPU collaboration to achieve higher system throughput. Its core philosophy is to minimize redundant computations and empower LLM application development in a relatively straightforward manner.

SGLang主要聚焦于两个层面：

  1. 复杂LLM程序编排：它不仅支持简单的问答，还能轻松处理多轮对话、任务规划、外部API调用以及生成JSON等结构化内容。
  2. 前后端协同设计：它采用一种前端领域特定语言（DSL）来简化编程逻辑，而后端运行时系统则专注于优化任务调度和多GPU协作，这种分离设计兼顾了灵活性与性能。

SGLang focuses on two main aspects:

  1. Orchestrating Complex LLM Programs: It supports not only simple Q&A but also handles multi-turn dialogues, task planning, external API calls, and generating structured content like JSON with ease.
  2. Cooperative Frontend-Backend Design: It employs a frontend Domain-Specific Language (DSL) to simplify programming logic, while the backend runtime system focuses on optimizing task scheduling and multi-GPU collaboration. This decoupled design balances flexibility and performance.

什么是 vLLM？

vLLM，全称 Vectorized Large Language Model Inference（向量化大型语言模型推理），是一个专为大模型推理和服务设计的高性能库。它在推理速度、内存效率和易用性方面进行了深度优化，因此成为部署诸如DeepSeek、Qwen、Llama等热门模型的常见选择。

vLLM, short for Vectorized Large Language Model Inference, is a high-performance library specifically designed for large model inference and serving. It is deeply optimized for inference speed, memory efficiency, and ease of use, making it a popular choice for deploying popular models like DeepSeek, Qwen, and Llama.

vLLM的设计重点在于：

  1. 内存效率与高吞吐：尤其在处理并发请求时，其设计旨在让模型推理更节省内存、运行更快速。
  2. 创新性核心技术：它引入了Paged Attention（分页注意力）和Continuous Batching（连续批处理）等技术，主要针对单轮推理场景下的效率与资源消耗问题进行优化。

The design focus of vLLM lies in:

  1. Memory Efficiency and High Throughput: Especially when handling concurrent requests, its design aims to make model inference more memory-efficient and faster.
  2. Innovative Core Technologies: It introduces technologies like Paged Attention and Continuous Batching, primarily optimizing for efficiency and resource consumption in single-turn inference scenarios.

关键技术深度剖析

SGLang 的核心技术

  1. RadixAttention（基数注意力）
    SGLang使用基数树（Radix Tree）来管理模型的KV（键值）缓存。这项技术允许多个请求共享先前已计算过的公共前缀（例如，对话历史），显著提高了缓存命中率。特别是在多轮对话场景下，缓存命中率据称可提升3到5倍，从而有效降低了推理延迟。

    SGLang uses a Radix Tree to manage the model's Key-Value (KV) cache. This technology allows multiple requests to share previously computed common prefixes (e.g., conversation history), significantly improving cache hit rates. Particularly in multi-turn dialogue scenarios, the cache hit rate is reported to increase by 3 to 5 times, effectively reducing inference latency.

  2. 结构化输出
    SGLang通过集成正则表达式等方式实现了约束解码功能，能够直接引导模型生成符合特定格式（如JSON、XML）的输出。这对于构建需要严格格式API接口或进行数据提取的应用非常便利。

    SGLang implements constrained decoding, for example by integrating regular expressions, which can directly guide the model to generate outputs conforming to specific formats (e.g., JSON, XML). This is highly convenient for building applications that require strict-format APIs or data extraction.

  3. 编译器与运行时分离
    其前端DSL允许开发者以声明式或程序式的方法描述复杂的生成逻辑，而后端编译器则将这些逻辑转换为高效的执行计划。这种前后端分离的架构使得系统既保持了编程的灵活性，又能通过后端进行深度优化。

    Its frontend DSL allows developers to describe complex generation logic in a declarative or programmatic way, while the backend compiler transforms this logic into efficient execution plans. This frontend-backend decoupled architecture maintains programming flexibility while enabling deep optimization at the backend.

vLLM 的核心技术

  1. Paged Attention（分页注意力）
    该技术借鉴了操作系统的虚拟内存分页思想，将连续的KV缓存分割成固定大小的“块”，并在GPU内存中动态分配和管理这些块。这种方法使得内存管理更加灵活，减少了内存碎片，据称可将内存效率提升3-4倍，从而支持更高的请求并发数。

    This technology borrows the concept of virtual memory paging from operating systems. It divides the continuous KV cache into fixed-size "blocks" and dynamically allocates and manages these blocks in GPU memory. This approach makes memory management more flexible, reduces memory fragmentation, and is reported to improve memory efficiency by 3-4 times, thereby supporting a higher number of concurrent requests.

  2. Continuous Batching（连续批处理）
    与传统静态批处理需要等待凑齐一批请求再处理不同，vLLM的连续批处理能够动态地将新到达的请求插入到正在执行的批次中。这确保了GPU计算单元持续处于工作状态，极大提高了硬件利用率和整体吞吐量。

    Unlike traditional static batching, which waits to collect a batch of requests before processing, vLLM's continuous batching can dynamically insert newly arrived requests into currently executing batches. This ensures that GPU computing units remain continuously occupied, greatly improving hardware utilization and overall throughput.

  3. 高效多卡并行
    在多GPU环境中，vLLM利用NCCL/MPI等通信库高效地切分和同步模型权重，并采用了诸如Zero Redundancy Tensor Parallelism等策略来优化内存使用和计算效率。

    In multi-GPU environments, vLLM utilizes communication libraries like NCCL/MPI to efficiently partition and synchronize model weights, and employs strategies such as Zero Redundancy Tensor Parallelism to optimize memory usage and computational efficiency.

性能基准测试分析

性能的优劣最终需要实测数据来验证。需要注意的是，不同的测试环境、模型和负载模式会导致结果存在差异。

The superiority of performance ultimately requires empirical benchmark data for verification. It is important to note that results can vary significantly depending on the test environment, model, and load pattern.

SGLang 的优势场景

  • 复杂任务处理：在涉及多轮对话、任务规划等场景中，SGLang表现突出。有测试表明，在Llama-7B模型上运行多轮对话时，其吞吐量可比vLLM高出5倍，这主要归功于RadixAttention对KV缓存的高效复用。
  • 格式化输出需求：对于需要生成严格JSON、XML等格式的应用（如智能客服、数据提取），SGLang内置的约束解码功能提供了原生便利。
  • 高并发下的稳定性：多项测试指出，随着并发请求数的增加，SGLang往往能保持更稳定的吞吐量。例如，在Llama3-70B的测试中，高并发下SGLang的吞吐量下降幅度远小于vLLM。
  • Complex Task Handling: SGLang excels in scenarios involving multi-turn dialogues, task planning, etc. Some tests indicate that when running multi-turn dialogues on the Llama-7B model, its throughput can be up to 5 times higher than vLLM, primarily due to the efficient reuse of KV cache by RadixAttention.
  • Formatted Output Requirements: For applications requiring strict generation of formats like JSON or XML (e.g., intelligent customer service, data extraction), SGLang's built-in constrained decoding offers native convenience.
  • Stability Under High Concurrency: Multiple tests suggest that as the number of concurrent requests increases, SGLang often maintains more stable throughput. For instance, in tests with Llama3-70B, SGLang's throughput degradation under high concurrency was significantly less than vLLM's.

vLLM 的优势场景

  • 单轮推理与低TTFT：在内容生成、单轮问答等场景，vLLM凭借其成熟优化，传统上具有强大竞争力。特别是在首词元延迟（TTFT - Time To First Token）方面，某些测试（如在Llama3.1 70B FP8模型上）显示vLLM能够提供更快的首次响应速度。
  • 内存效率优先：对于希望在有限GPU内存内部署更大模型或服务更多用户的场景，vLLM的Paged Attention技术带来的内存效率提升是一个关键优势。
  • 生态成熟与易集成：vLLM发展较早，拥有更成熟的API和更广泛的社区集成，对于追求快速上线的项目更为友好。
  • Single-Turn Inference and Low TTFT: In scenarios like content generation and single-turn Q&A, vLLM, with its mature optimizations, has traditionally been highly competitive. Particularly regarding Time To First Token (TTFT), some tests (e.g., on the Llama3.1 70B FP8 model) show that vLLM can provide faster initial response times.
  • Memory Efficiency Priority: For scenarios where the goal is to deploy larger models or serve more users within limited GPU memory, the memory efficiency gains from vLLM's Paged Attention technology are a key advantage.
  • Mature Ecosystem and Easy Integration: vLLM has been developed earlier, boasting more mature APIs and broader community integration, making it more friendly for projects requiring rapid deployment.

多GPU支持策略对比

  • SGLang：支持张量并行和数据并行。其RadixAttention技术可以跨GPU共享缓存，有助于减少多卡计算时的冗余。
  • vLLM：同样支持张量并行（宣称零冗余优化），并具备分布式调度器，能智能地将请求分配至不同GPU，甚至支持跨机器的流水线并行，扩展性较强。
  • SGLang: Supports tensor parallelism and data parallelism. Its RadixAttention technology can share cache across GPUs, helping to reduce redundancy in multi-GPU computations.
  • vLLM: Also supports tensor parallelism (advertised with zero-redundancy optimization) and features a distributed scheduler that intelligently allocates requests to different GPUs. It even supports pipeline parallelism across machines, indicating strong scalability.

选型建议总结

选择SGLang还是vLLM，应基于具体的应用需求、技术栈和资源约束。

The choice between SGLang and vLLM should be based on specific application requirements, technology stack, and resource constraints.

何时选择 SGLang？

  • 场景复杂：需要构建高级聊天机器人、多步推理的智能体（Agent），或涉及复杂程序化调用的应用。
  • 输出需严格格式化：API或下游处理强制要求JSON、XML等特定格式。
  • 预期并发高，且重视吞吐稳定性：从最新测试看，在高并发负载下，SGLang可能提供更优且更稳定的吞吐性能。
  • Complex Scenarios: When building advanced chatbots, agents requiring multi-step reasoning, or applications involving complex programmatic calls.
  • Strictly Formatted Output Required: When APIs or downstream processing mandate specific formats like JSON or XML.
  • High Expected Concurrency with Emphasis on Throughput Stability: Based on recent tests, SGLang may offer better and more stable throughput performance under high concurrent loads.

何时选择 vLLM？

  • 任务以单轮推理为主：如批量内容生成、翻译、摘要，且对首字响应速度（TTFT）极其敏感。
  • 资源紧张，需最大化内存效率：在有限显存下，希望服务更大模型或更多并发。
  • 追求快速集成与部署：需要利用其成熟的API和广泛的社区支持，加速项目上线进程。
  • Primarily Single-Turn Inference Tasks: Such as batch content generation, translation, summarization, where time to first token (TTFT) is critically important.
  • Resource-Constrained, Needing Maximized Memory Efficiency: When needing to serve larger models or more concurrent requests within limited GPU memory.
  • Pursuing Rapid Integration and Deployment: When leveraging its mature APIs and broad community support to accelerate project launch.

最终建议

综合现有性能评测，SGLang在复杂任务处理和高并发吞吐稳定性方面展现出显著潜力，而vLLM在单轮推理的内存效率与低延迟响应上仍有其坚固优势。

Synthesizing existing performance evaluations, SGLang shows significant potential in handling complex tasks and maintaining throughput stability under high concurrency, while vLLM retains its solid advantages in memory efficiency and low-latency response for single-turn inference.

没有放之四海而皆准的答案。最可靠的方法是在您自己的目标硬件配置和业务负载特征下，对两个框架进行实际的基准测试（POC）。通过对比关键指标（如TTFT、吞吐量、延迟分布、内存占用），做出最适合自身技术栈和业务目标的决策。

There is no one-size-fits-all answer. The most reliable approach is to conduct actual proof-of-concept (POC) benchmarks with both frameworks under your own target hardware configuration and business load characteristics. By comparing key metrics (such as TTFT, throughput, latency distribution, memory footprint), you can make the decision that best suits your technology stack and business objectives.

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