sgl-project/sglang vs vllm-project/vllm
Compare sgl-project/sglang and vllm-project/vllm using the current verified snapshot: positioning, license, deployment, use cases, limitations, and original sources.
sgl-project/sglang
SGLang is a serving framework designed for low-latency, high-throughput inference of large language and multimodal models across diverse hardware. It provides a fast runtime with RadixAttention, continuous batching, and distributed parallelism for AI engineers and operations teams building model APIs.
- License
- Apache-2.0
- Deployment
- Refer to project documentation
- Use cases
- Image Processing
- Updated
- 2026-07-17T10:00:18Z
vllm-project/vllm
vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. It provides an OpenAI-compatible API server and uses PagedAttention to manage attention key and value memory efficiently.
- License
- Apache-2.0
- Deployment
- Python environment
- Use cases
- Developers, AI engineers, and operations teams needing high-throughput and memory-efficient inference for large language models. · Users requiring distributed inference with tensor, pipeline, data, expert, and context parallelism.
- Updated
- 2026-07-17T04:38:12Z
How to choose
First eliminate options that fail required deployment, license, or use-case constraints; then inspect each detail page for limitations and direct evidence.