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

Large Language Model inference presents unique computational challenges that traditional optimization approaches fail to address effectively. The autoregressive nature of text generation, massive model sizes exceeding available GPU memory, and highly variable request patterns create a perfect storm of inefficiency—production deployments commonly achieve only 15-30% GPU utilization while facing latency constraints that preclude simple batching strategies. This paper presents comprehensive optimi...