Stateful Routing Beats Load Balancing for LLM Inference
AccBalanced · x · 2026-07-20
This piece explores why traditional stateless L7 load balancing leads to GPU inefficiencies in LLM inference serving, pointing out that stateful routing is becoming the industry standard.
- Cache Conflicts & Latency: Traditional routing (like Round Robin) scatters a user's sequential requests across different GPUs, destroying KV Cache locality. This forces prefill recalculations every time, drastically spiking Time To First Token (TTFT).
- Cost Penalties: For instance, OpenAI offers a 50% discount on identical prefixes; poor routing directly misses out on this cost optimization.
- The Solution: Top teams are adopting stateful routing architectures to ensure requests are precisely dispatched to cached nodes, thereby boosting overall inference throughput and efficiency.
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