vLLM vs SGLang vs LMDeploy: Fastest LLM Inference Engine in 2026?

In 2026, SGLang and LMDeploy emerge as the leading LLM inference engines, achieving approximately 16,200 tokens per second on H100 GPUs, with vLLM trailing at 12,500 tokens per second, a 29% gap that can translate into significant GPU savings. SGLang is optimized for multi-turn conversations with its RadixAttention, LMDeploy excels in quantized model serving using its TurboMind engine, and vLLM offers the most mature ecosystem for general production use through its PagedAttention, which optimizes memory utilization. Each engine employs distinct architectures catering to different workloads, and the choice between them should be guided by the specific needs of the application, such as throughput, latency, and model compatibility. The landscape of inference engines has matured, with vLLM providing a stable foundation, SGLang enhancing multi-turn interactions, and LMDeploy optimizing for speed on constrained hardware, highlighting the importance of selecting the right engine based on use case and traffic patterns to optimize costs and performance.

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