Should I choose vllm-project/vllm or sgl-project/sglang for serving large language models?

When it comes to serving large language models (LLMs), vllm-project/vllm stands out as the clear winner for speed and efficiency. Despite strong competition from sgl-project/sglang, vllm's superior features, larger community, and active maintenance make it the better choice for serious users.

Comparison Overview

Key Insights

• Maintenance & Activity: Both projects are actively maintained, with significant updates in the last few days, but vllm has a much larger contributor base (3,610 vs. 1,985), suggesting a more robust community and development pipeline.

• Feature Highlights:

  • vllm excels with features such as:

    • State-of-the-art serving throughput and efficient memory management using techniques like PagedAttention.
    • Seamless integration with 200+ models on Hugging Face, making it a versatile choice for diverse applications.
    • Extensive optimizations for speed across various hardware platforms (NVIDIA, AMD, Intel, TPUs, etc.).

  • sgl also offers strong performance and is tailored for integrating both LLMs and multimodal models. It features a cooking pot of frameworks for diverse applications (RL, Diffusion, Audio, etc.), but it lacks the same breadth of optimization and community engagement as vllm.

Recommendation

Choose vllm-project/vllm if you prioritize maximum performance and community support for serving LLMs. Its significant contributor base and diverse optimizations are backed by extensive documentation and active community involvement—elements critical for scaling and maintaining models in production.

Conversely, opt for sgl-project/sglang if your focus leans towards multimodal capabilities or specific use cases in reinforcement learning and agentic workloads. However, its relatively smaller community may pose risks for long-term support.

In summary: vllm wins for pure LLM serving; sgl is for niche scenarios.