Why LLM Inference Needs a New Kind of Router
9 hours ago
- #Load Balancing
- #LLM Inference
- #Modular Cloud
- Traditional HTTP routing algorithms like round-robin, consistent hashing, and least-connections assume stateless, interchangeable backends, which fail for LLM inference due to stateful GPU pods with KV caches.
- LLM inference introduces four key challenges: KV cache state affecting prefill latency, hardware specialization between prefill (compute-bound) and decode (memory-bandwidth-bound) phases, multi-turn conversations requiring session affinity for cache reuse, and multi-step execution needing coordination across backends.
- Modular Cloud's routing solution addresses these with three architectural layers: a data layer for microsecond cache state tracking, a decision layer with composable plugins for routing logic, and an execution layer for multi-step request coordination.
- This approach enables prefix-aware routing and supports various deployment patterns through profile configurations, building on advancements from systems like NVIDIA Dynamo and vLLM, without requiring new algorithms for each pattern.