How GPU Batching Slashed LLM Eval Costs by 60%

Teams spinning up dedicated GPUs for every PR's LLM evaluation face massive idle waste—often 70% unutilized time. By pooling warm GPU instances and batching inference requests through a queue, teams can cut evaluation costs by 60% while maintaining CI speed. As LLM-powered evaluations become standard in development workflows, this optimization pattern is becoming essential for cost-conscious teams.