Optimizing LLM Workloads: A New Efficiency Frontier
infrastructure#llm📝 Blog|Analyzed: Feb 22, 2026 15:17•
Published: Feb 22, 2026 15:07
•1 min read
•r/mlopsAnalysis
This post highlights an intriguing challenge in serverless environments: the discrepancy between actual inference time and billed time for Large Language Model (LLM) workloads. The insights shared offer a valuable starting point for optimizing model deployments and reducing costs, promising more efficient resource utilization.
Key Takeaways
- •The primary contributor to the difference between execution and billed time is model reloading, idle retention, and scaling behavior.
- •Teams deploying multiple models or dealing with long-tail deployments are likely to experience similar overhead.
- •The post sparks a discussion on aligning billing with actual LLM execution time to improve cost efficiency.
Reference / Citation
View Original"We profiled a 25B-equivalent workload recently. ~8 minutes actual inference time ~100+ minutes billed time under a typical serverless setup"