Templated from LOIS: Learning-Optimized Inference System for Cost-Efficient Large Language Model Deployment

This paper presents LOIS (Learning-Optimized Inference System), a novel self-optimizing infrastructure framework that dynamically adapts Large Language Model deployment strategies through reinforcement learning and predictive analytics. LOIS addresses the critical challenge that inference costs represent 60-80% of total AI operational expenditure in production systems. The framework incorporates four key innovations: adaptive model routing using learned policies, dynamic quantization that adjusts precision in real-time, predictive resource allocation through LSTM-based workload forecasting, and multi-tenant optimization for compute sharing. Our evaluation combines analytical modeling and discrete-event simulation on production-representative workloads (347K queries over 7 days), indicating projected improvements of 47% reduction in inference costs, 34% improvement in P95 latency, and 3.2x better GPU utilization compared to baseline Kubernetes orchestration. While results are simulation-based rather than live deployment, the methodology employs empirically-validated performance models from established systems (vLLM, TensorRT-LLM) and conservative assumptions grounded in published benchmarks. LOIS represents a paradigm shift from reactive infrastructure management to proactive, learning-based optimization for economically sustainable LLM deployment at scale.