arXiv:2607.05876v1 Announce Type: cross Abstract: LLM serving optimization typically benchmarks many configurations and reaches for heavy profilers when latency targets are missed. We argue for the reverse discipline: estimation is the analytical layer of profiling -- without it, optimization degenerates to grid search. Floor First is a residual-driven triage workflow. Each decode step is modeled as a five-dimensional resource vector (HBM bytes, FLOPs, network bytes, network messages, KV capacity); summing within a resource and maximizing across resources gives an optimistic floor, the plain s
The increasing scale and complexity of LLMs are pushing the limits of current serving infrastructure, making efficient optimization techniques critical for practical deployment.
This research outlines a structured methodology for optimizing LLM serving, moving beyond inefficient grid-search approaches, which will directly impact the cost and performance of AI applications.
The proposed 'Floor First' triage workflow introduces an analytical pre-profiling step, allowing for more targeted and efficient optimization of LLM configurations rather than brute-force benchmarking.
- · Cloud providers
- · LLM developers
- · AI-powered application companies
- · MLOps platforms
- · Companies relying on inefficient LLM serving
- · Pure grid-search optimization tools
More cost-effective and performant deployment of large language models becomes widely accessible.
Accelerated adoption of advanced LLMs in diverse applications as inference costs decrease and latency improves.
Enhanced competition in the AI services market due to lower barriers to entry for deploying complex models efficiently.
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Read at arXiv cs.AI