How TNG Broke vLLM’s FIFO Queue to Stop Power Users from Hogging GPUs

If you’ve ever waited for a GPU while a single user floods an inference engine with hundreds of requests, you know the pain. TNG Technology Consulting just open-sourced a practical fix that doesn’t require touching the model itself, and it’s a pattern worth stealing.

The core problem is embarrassingly simple. Inference backends like vLLM use a first-in, first-out queue to batch requests for the GPU. That batching is great for throughput, but it’s socially stupid. A single “power user” can fire off a massive batch of requests, and everyone else gets stuck behind them in line. TNG’s engineers realized you can’t fix this inside vLLM—once a request hits the backend’s queue, you’ve lost control of ordering. The only leverage point is upstream.

Their solution puts a custom API server, which they call the “LLM-Server,” in front of the inference engine. Instead of one global queue, each user gets their own queue, and a round-robin scheduler pulls from them fairly. The result: a new user sending a single request can jump ahead of a power user who already has three requests waiting. It’s not about raw throughput—it’s about latency fairness, and TNG argues that’s what actually matters in a multi-tenant environment.

The approach gets cleverer when you consider backpressure. If the LLM-Server just forwarded requests as fast as it could, they’d all pile up in vLLM’s FIFO queue again, defeating the whole point. Since vLLM doesn’t expose a way to cap its internal queue length, TNG dynamically throttles the rate it sends requests to the backend. The goal is explicitly stated: keep that backend queue as short as possible so the fair-scheduling decisions made upstream actually stick. This is the kind of operational detail that separates a blog post from a production-ready system.

What I find most useful here is the clear-eyed acknowledgment of what they can’t solve. Estimating generation length to prioritize short requests over long ones sounds great until you realize chat-based AI assistants don’t have token limits. KV-cache-aware routing—grouping similar prompts to maximize cache hits—shows real promise and is getting attention from NVIDIA’s Dynamo and AIBrix, but it’s a separate layer of optimization. TNG instead focuses on what’s controllable: user-level fairness and backpressure. For teams running shared LLM infrastructure where a few heavy users can ruin the experience for everyone else, this is a dead-simple architectural pattern that costs little to implement and makes the resource contention problem visibly go away.