FLARE: Fine-Grained Length-Aware Routing for Resource-Efficient Heterogeneous LLM Serving (2026.acl-long)
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| Challenge: | Existing routers lack fine-grained resource awareness across deployment settings, which degrades efficiency metrics in real-world serving. |
| Approach: | They propose a length-centric, resource-aware multi-LLM routing framework that uses length-based models to estimate per-query latency and cost. |
| Outcome: | Experiments show that FLARE reduces latency and cost by up to 68% and 75% while maintaining competitive accuracy. |
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