I'm trying to understand the technical architecture. Is SGLang faster than vLLM for agent workflows because of how it handles the KV cache? Specifically, how does RadixAttention manage memory during long multi-turn sessions?
3 answers
The fundamental difference is how they treat the "prefix." vLLM's PagedAttention is great for fixed-length memory blocks, but it doesn't naturally understand the "tree" structure of a conversation. SGLang uses a Radix Tree to store the KV cache. When an agent forks into different reasoning paths or starts a new turn, SGLang looks at the tree and says, "I already have the first 1500 tokens of this prompt in memory," and instantly reuses it. This makes SGLang faster than vLLM for agent workflows where the prompt grows incrementally. It avoids the redundant computation that usually plagues long-running agent sessions.
Cynthia, does this mean SGLang uses more VRAM because it’s holding onto these "branches" of the conversation? Or is the memory management efficient enough for consumer GPUs?
RadixAttention is more dynamic. It caches everything automatically, which is why it feels much faster during interactive agent loops.
Exactly. The "zero-config" aspect is the best part. You don't have to manually tell it what to cache; the radix tree handles it all.
Ryan, it actually uses memory more efficiently. Because it reuses the same physical blocks for shared prefixes, it prevents duplication. You can actually fit more concurrent agent sessions into the same VRAM compared to a standard setup without prefix caching.