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

In 2026, SGLang and LMDeploy are leading the field of LLM inference engines, each achieving around 16,200 tokens per second on H100 GPUs, with vLLM trailing at 12,500 tokens per second, a 29% difference that can translate to significant cost savings. The choice of engine depends on specific use cases: SGLang is best for multi-turn conversations, LMDeploy excels in serving quantized models, and vLLM is preferred for its mature ecosystem, broad model compatibility, and ease of deployment. These engines adopt different architectural approaches, such as SGLang's RadixAttention for efficient prefix matching and LMDeploy's TurboMind for optimized speed, especially in latency-sensitive scenarios. Benchmark tests across multiple engines reveal that while vLLM offers the broadest model support, SGLang and LMDeploy provide superior raw throughput, with each engine excelling in specific scenarios, highlighting the importance of matching engine capabilities to workload requirements to optimize performance and costs.