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ResearchSeptember 17, 2026

The Inference Engineering Pareto Atlas: Which Optimizations Dominate the Cost, Quality, and Latency Frontier?

LLM inference optimizations report speedups on different models, GPUs, prompts, and quality metrics, making them hard to compare or combine. We build a cost, quality, and latency Pareto atlas to identify the best configurations for different deployment constraints. Since…

Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri, Sai Pavan Kumar, Sai Kapil Kumar, Ramesh Nampelly·arXiv — cs.AI
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Key Takeaways
  • 01.LLM inference optimizations report speedups on different models, GPUs, prompts, and quality metrics, making them hard to compare or combine
  • 02.We build a cost, quality, and latency Pareto atlas to identify the best configurations for different deployment constraints

This material is provided as an extended analytical summary.

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