sglang

Why we included this project

SGLang has become a default choice for teams that want to serve open language and multimodal models on their own hardware, and it earns that spot by handling the unglamorous parts of the serving stack itself: request batching, KV-cache management, structured output decoding, and spreading a workload across many GPUs. That means you can stand up an OpenAI-compatible endpoint for DeepSeek, Llama, Qwen, or GLM without wiring the plumbing by hand. Its RadixAttention prefix caching and model-specific kernels show up as measurable throughput gains on NVIDIA and AMD accelerators, and the project has since grown to cover diffusion-based image and video generation. If you are comparing runtimes for a serious deployment, SGLang is one of the first engines worth putting on the bench, especially when you want to run a freshly released checkpoint the week it ships.