Efficient Request Queueing – Optimizing LLM Performance

Source: HuggingFace Date: 2025-04-02 URL: https://huggingface.co/blog/tngtech/llm-performance-request-queueing

Summary

Technical guide: LLM inference request queueing best practices from TNG Technology Consulting. Two core problems: (1) FIFO queuing lets power users starve others — solution is per-user queues with round-robin scheduling upstream of vLLM/TGI; (2) fair scheduling alone doesn’t prevent backend accumulation — solution is Prometheus-based backpressure monitoring against vLLM’s /metrics endpoint, throttling sends when queue depth exceeds ~3 or per-output-token latency exceeds 150ms. Also covers vLLM’s native priority scheduling for eviction-based batching. Part 1 of a series; part 2 covers prefill/decode phase optimization.

Implications

Open-weights ecosystem health. The per-user queue + backpressure pattern describes production multi-tenant LLM serving that the inference frameworks themselves don’t solve out of the box — teams discovering this empirically after vLLM deployment will recognize the problem immediately. The Prometheus-metric-driven backpressure approach is a reproducible operations pattern.

Transformers library trajectory. The mention of NVIDIA Dynamo and AIBrix as KV-cache-aware routers points to where production multi-instance inference is heading: scheduling decisions that account for KV cache state, not just queue depth. This is the next layer of inference optimization that will matter once teams have solved the basic queueing problem.