Are Large Language Models Economically Viable for Industry Deployment?
Generative AI-powered by Large Language Models (LLMs)-is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conversational automation, where reliability, efficiency, and cost control are critical. In such settings, models must satisfy strict constraints on energy, latency, and hardware utilization-not accuracy alone. Yet prevailing evaluation pipelines remain accuracy-centric, creating a Deployment-Evaluation Gap-the absence of operational and economic criteria in model assessment. To address this gap, we present EDGE-EVAL-a industry-oriented benchmarking framework that evaluates LLMs across their full lifecycle on legacy N...
| Authors | Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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