GPU Efficiency Without Compromise: LLM Inference Optimization Through Dynamic Batching, KV-Cache Management, and Intelligent Scheduling

Large Language Model (LLM) inference at scale faces critical bottlenecks in GPU utilization, latency, and cost-per-token that threaten the economic viability of production AI deployments. This paper presents a comprehensive optimization framework for production LLM inference systems, integrating four synergistic techniques: phase-aware scheduling, dynamic batching, multi-tier KV-cache memory optimization, and quantization techniques. Through rigorous analysis of production deployments, we demonstrate that organizations can achieve 3-10x improvements in throughput while reducing inference costs by 30-50% without sacrificing output quality.