Tri-Model Intelligent Framework for AI Infrastructure Orchestration

Luv Garg

Abstract: The exponential deployment of AI in 2026 has shifted focus from accuracy to sustainable operations. Organizations face a trilemma: minimizing latency, maximizing computational throughput, and reducing operational costs. We propose a Tri-Modal Intelligent Framework integrating Edge Computing for sub-5ms real-time response, Cloud Computing for scalable distributed training, and Quantum Computing for NP-hard optimization. A Deep Reinforcement Learning agent using Proximal Policy Optimization dynamically orchestrates tasks, optimizing latency, cost, and throughput. Simulation results indicate a 42% reduction in end-to-end latency, 35% operational cost savings, $52,500 annual savings for mid-sized deployments, and 35% reduction in carbon footprint. This framework provides a scalable, sustainable approach to hybrid AI infrastructure.

Keywords: Edge Computing, Cloud Computing, Quantum Computing, Deep Reinforcement Learning, Resource Orchestration, AI Infrastructure, Proximal Policy Optimization, Multi- Modal Systems, Sustainable AI, Cloud Economics

How to Cite?: Luv Garg, "Tri-Model Intelligent Framework for AI Infrastructure Orchestration", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 1282-1283, https://www.ijsr.net/getabstract.php?paperid=SR26323003850, DOI: https://dx.dx.doi.org/10.21275/SR26323003850

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