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Explore the vLLM vs SGLang benchmarks to understand performance differences in LLMs and framework comparisons.

Continuing the series, this one compares between two inference engines, SGLang and vLLM, and provides guidance on choosing the right one based on project requirements. Use this as guide to drive your decisions.

The emergence of vLLM improved GPU utilization for AI model serving, addressing issues like memory fragmentation. However, as workloads evolved towards complex multi-turn interactions, SGLang was developed to optimize prefix caching, reducing unnecessary computations. This ensures higher efficiency, lower latency, and reliable structured outputs such as JSON, enhancing modern AI applications.

SGLang and vLLM are two tools developed to enhance the efficiency of using open-source AI models. While vLLM addresses resource wastage in AI model hosting, SGLang improves response times by minimizing repetition in processing requests. Here’s a read to help you decide which one is better.

This blog discusses inefficiencies in GPU utilization for language model serving due to memory management issues, notably the KV cache. It highlights five core problems that impact throughput and proposes solutions.

We benchmarked five models on the NVIDIA B300 – from Gemma-4-26B to GLM-5.2 at 753 billion parameters, across 18 runs under real production concurrency. The throughput numbers are strong. The TTFT finding is counterintuitive. And the infrastructure implication is bigger than either.