SGLang vs vLLM: A Comprehensive Comparison
Introduction
With the growing popularity of large language models (LLMs), efficiently deploying and performing inference has become a key concern. SGLang and vLLM, as two leading inference frameworks, each have their own strengths in performance optimization, multi-GPU collaboration, and applicable scenarios.
This article will compare them from multiple perspectives, including design goals, core technologies, performance benchmarks, and multi-GPU support, to help you make an informed decision quickly.
What's SGLang?
SGLang is an open-source inference engine designed by the SGLang team to address these challenges. It optimizes CPU and GPU resources during inference, achieving significantly higher throughput than many competitive solutions. Its design utilizes an innovative approach that reduces redundant computations and enhances overall efficiency, thereby enabling organizations to manage better the complexities associated with LLM deployment.
Full Name: Structured Generation Language
Development Team: UC Berkeley
Design Objectives:
- Support complex LLM Programs (programmatic LLM calls), such as multi-turn conversations, planning, tool calling, and structured outputs (e.g., JSON).
- Enhance flexibility and performance across multi-GPU nodes through co-design of the frontend language and backend runtime.
What's vLLM?
vLLM is a high-performance library for LLM (Large Language Model) inference and serving. It is optimized for speed, efficiency, and ease of use, making it ideal for deploying models like DeepSeek, Qwen, Gemma, Phi, LLaMA, GPT, and others.
Full Name: Vectorized Large Language Model Inference
Development Team: UC Berkeley
Design Objectives:
- Optimize memory efficiency and throughput for large model inference, particularly for high-concurrency scenarios.
- Address efficiency and resource bottlenecks in single-round inference through paged memory management and dynamic batching.
Main Differences between SGLang and vLLM
SGLang’s Key Innovations
1. RadixAttention
- Uses a Radix Tree to manage KV Cache, enabling prefix sharing and reuse in multi-turn conversations.
- Impact: Improves cache hit rate by 3-5x in multi-turn tasks, significantly reducing latency.
2. Structured Output Support
- Implements constrained decoding via regex and finite-state machines (FSM) to directly generate structured data (e.g., JSON).
3. Compiler-Inspired Design
- Frontend DSL (Domain-Specific Language) simplifies programming for complex tasks.
- Backend runtime optimizes scheduling and resource allocation.
vLLM’s Core Concepts
1. PagedAttention
- Borrows from OS paging mechanisms, splitting KV Cache into fixed-size blocks for dynamic GPU memory allocation.
- Impact: Boosts memory efficiency by 3-4x, supporting higher concurrency.
2. Continuous Batching
- Dynamically adjusts batch sizes, splitting requests into prefill and decode phases to maximize GPU utilization.
3. Zero Redundancy Tensor Parallelism
- Leverages NCCL/MPI for efficient weight partitioning and synchronization across multiple GPUs, improving compute efficiency.
Use Cases and Performance Benchmarks
SGLang’s Strengths
Best for:
- Complex tasks: Multi-turn conversations, planning, and tool calling (e.g., API/database integration).
- Structured outputs: Tasks requiring JSON/XML generation (e.g., customer support bots, data analysis).
Performance Data:
- 5x higher throughput than vLLM in multi-turn dialogue tasks (Llama-7B).
- 30%-50% lower latency (thanks to RadixAttention’s cache reuse).
vLLM’s Strengths
Best for:
- High-throughput single-round inference: Content generation, recommendation systems, single-turn Q&A.
Performance Data:
- 14-24x higher throughput compared to HuggingFace Transformers.
- Supports 100+ concurrent requests per GPU (enabled by PagedAttention).
Multi-GPU Coordination Mechanisms
SGLang’s Multi-GPU Strategy
Tensor Parallelism: Splits model weights across GPUs (e.g., --tp 8 for 8-GPU parallelism).
Data Parallelism: Shards input data and balances workloads with continuous batching.
Cache Sharing: RadixAttention enables cross-GPU prefix caching, minimizing redundant computation.
vLLM’s Multi-GPU Strategy
Tensor Parallelism: Similar to SGLang but optimized for zero-redundancy memory allocation.
Distributed Scheduler: Dynamically routes requests to GPUs and supports preemption (offloading some requests to CPU).
Multi-Node Scaling: Deploys on k8s clusters and scales via pipeline parallelism across servers.
Framework Selection Guide
When to Choose SGLang?
For complex multi-turn interactions (e.g., dialogue systems, planning agents).
When structured outputs are required (e.g., API responses must strictly follow JSON/XML schemas).
For deep customization of generation logic or optimizing cache reuse (e.g., leveraging RadixAttention).
When to Choose vLLM?
For high-concurrency single-round tasks (e.g., batch content generation, real-time Q&A).
When maximizing throughput under limited resources (e.g., small/medium teams deploying billion-parameter models).
For quick integration into existing pipelines (vLLM has more mature APIs and community support).
Summary
| Features | SGLang | vLLM |
|---|---|---|
| Core Strength | Multi-turn dialogue, structured output, complex task optimization | High-throughput single-round inference, memory-efficient management |
| Key Tech | RadixAttention, compiler-inspired design | PagedAttention, Continuous Batching |
| Suitable Models | General LLMs/VLMs (e.g., LLaMA, DeepSeek) | Ultra-large-scale LLMs (e.g., GPT-4, Mixtral) |
| Learning Curve | Higher (requires custom DSL) | Lower (ready-to-use) |
Additional Resources
SGLang GitHub: https://github.com/sgl-project/sglang
vLLM GitHub: https://github.com/vllm-project/vllm
SGLang Official Documentation: https://docs.sglang.ai
vLLM Official Documentation: https://docs.vllm.ai/en/latest/