Open-source LLM inference engines compared: SGLang, vLLM, MAX, and BentoML 2026

The text compares three leading inference engines—SGLang, vLLM, and MAX (Modular)—highlighting their core features, performance metrics, and specific use cases as they head into late 2026. SGLang, developed by RadixArk, excels in multi-turn chatbots and structured outputs due to its innovative RadixAttention and xgrammar backend, while being supported by a commercial startup valued at $400 million. vLLM, known for its PagedAttention innovation, is the most adopted in industry, boasting broad model and hardware support, and a robust community, making it a reliable choice for large-scale production systems. MAX, from Modular AI, distinguishes itself with a fully vertically integrated stack that eliminates CUDA dependencies, offering hardware portability and the smallest container footprint, making it suitable for multi-hardware environments and custom kernel development. Each engine caters to different deployment needs, with SGLang offering speed in specific workloads, vLLM prioritizing stability and wide compatibility, and MAX providing flexibility and simplicity through its compiler-driven approach. The text notes the rapid evolution of inference technologies, with disaggregated prefill/decode becoming standard and multi-modal serving expanding, while commercial consolidation signals a shift toward enterprise monetization in the open-source inference market.

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