Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference
Production vLLM fleets provision every instance for worst-case context length, wasting 4-8x concurrency on the 80-95% of requests that are short and simultaneously triggering KV-cache failures -- OOM crashes, preemption storms, and request rejections. Both problems share a single root cause: configuration-traffic mismatch. We propose token-budget-aware pool routing: estimate each request's total token budget using a self-calibrating per-category bytes-per-token ratio, then dispatch it to one of two vLLM pools -- a high-throughput short pool or a high-capacity long pool -- each right-sized for its workload class. The ratio is learned online via exponential moving average from usage.prompt_...
| Authors | Huamin Chen, Xunzhuo Liu, Junchen Jiang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 March 2026 |
| Last updated at source | 14 April 2026 |
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