aiqbench analysis
Inference server failures often stem from KV cache memory exhaustion triggered by concurrency spikes rather than compute saturation or prompt length alone. Since the cache size scales linearly with concurrent requests, naive management strategies can waste up to 80% of reserved VRAM, making traffic-aware optimizations like paged allocation critical for scalability. Consequently, sizing a deployment requires treating the KV cache as a dynamic variable driven by request volume, not just a static cost tied to model weights.
The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute
A VRAM budget formula for LLM serving, and three optimization strategies mapped to the traffic patterns that trigger the OOM. The post The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute appeared first on Towards Data Science.
VRAM used to be a training-time worry. You sized your cluster for the weights, the optimizer states, and the gradients, and once the model was trained, the memory math felt settled. Serving looked cheap by comparison: load the weights, run forward passes, done. Then you put the model behind real traffic, and it fell over at a load that made no sense. I was sizing an inference deployment for a mid-sized model on a shared GPU pool. The weights fit with room to spare, and the server ran fine in testing. Under concurrent traffic, it returned CUDA out-of-memory errors while GPU compute utilization sat far below saturation. The first instinct was that the model was too big. It wasn't. The key-value cache was eating the headroom, and it grew with every concurrent request. I turned on paged allocation and prefix caching before adding a single…
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