Where capacity goes unused
Poor batching, incorrect model placement, oversized instances, idle replicas, inefficient routing, and static capacity can leave expensive GPU resources underutilized.
Workload-aware capacity
Request volume, queue depth, concurrency, GPU utilization, latency, and token throughput are relevant scaling signals. Replica bounds, thresholds, and cooldowns help express operational policies.
Iterate with evidence
Analyze cost and utilization alongside serving performance. Refine runtime settings, batching, precision, concurrency, and scaling configuration according to the needs of the model and application.
Based on InferGrid’s company and product context.