LLM Inference Servers Compared: vLLM vs TGI vs SGLang vs Triton (2026)

Inference server performance plays a crucial role in the efficiency of machine learning models, with vLLM, SGLang, TGI, and Triton being key players in 2026. vLLM, originating from UC Berkeley's Sky Computing Lab, is favored for its PagedAttention mechanism, which optimizes memory use and supports a wide range of hardware, making it a reliable choice for high-concurrency environments. SGLang, developed by LMSYS, excels in multi-turn conversations and agent workflows with its RadixAttention feature, offering significant performance improvements in shared-context scenarios. TGI has entered maintenance mode, with Hugging Face recommending migration to vLLM or SGLang for new deployments due to the end of new feature development. Triton, focused on enterprise settings, offers robust multi-model serving capabilities but requires significant setup expertise. For batch inference, SGLang and LMDeploy outperform vLLM, while vLLM remains versatile and well-documented, making it suitable for diverse workloads. The decision to choose between these servers depends on specific use cases, such as batch processing, conversational AI, or enterprise-level integration, balanced against team expertise and infrastructure complexity.