Does RadixAttention make SGLang scale better than vLLM?

All answers to this question.

RadixAttention provides a massive memory advantage specifically when workloads feature significant prefix sharing. In classic production environments where 100 users or agents pass requests containing identical system instructions or long context documents, vLLM's PagedAttention still allocates separate, isolated cache pages for each concurrent stream. SGLang’s radix tree allows multiple concurrent requests to point directly to the exact same physical memory nodes for their shared prefixes. This drastically reduces the total VRAM footprint, allowing for higher batch sizes and better overall concurrency scaling.

   Answered 2025-07-05 by Hannah Sterling

Are there specific hardware constraints where this scaling advantage breaks down? I heard that SGLang’s specialized kernels are hyper-optimized for the NVIDIA CUDA ecosystem, but what happens if you try to deploy this framework across a heterogeneous cluster containing AMD or TPU accelerators?

   Answered 2025-07-09 by Bradley Cooper

• Bradley Cooper You hit on a crucial bottleneck. SGLang's most aggressive optimizations rely on FlashInfer and specialized CUDA kernels, making it less performant on non-NVIDIA setups. vLLM has a much more mature abstraction layer with stable production support for ROCm, TPUs, and diverse hardware architectures.

     Commented 2025-07-11 by Christian Slater

For shared-prefix operations, the radix tree architecture scales exponentially better by eliminating duplicate data processing inside the GPU.

   Answered 2025-07-15 by Dylan Matthews

• Dylan Matthews Spot on. We observed that as concurrency scaled up to 50 parallel agent streams, our VRAM overhead remained relatively stable under SGLang, whereas our vLLM instances suffered frequent out-of-memory crashes due to linear cache replication.

     Commented 2025-07-18 by Hannah Sterling