SGLang Challenges vLLM With RadixAttention as AI Inference Efficiency Takes Center Stage

As AI workloads shift from model training to deployment, inference runtime efficiency has become the defining factor for cost and scalability of AI products. For years, vLLM set the industry standard with its PagedAttention algorithm, which managed GPU memory (VRAM) by treating the Key-Value Cache like a paged virtual memory system. However, the rise of autonomous agents, multi-step reasoning pipelines, and repeated tool calls exposed the limitations of static block paging. SGLang emerged as a strong competitor by introducing RadixAttention, a prefix-tree data structure that indexes the KV Cache hierarchically across requests, enabling zero-cost reuse of shared prompt prefixes. Both runtimes have also been boosted by advances in Speculative Decoding techniques such as EAGLE 3.1 and Medusa, which allow multiple candidate tokens to be generated and verified in parallel, significantly improving throughput on hardware like the NVIDIA H100 and H200.