A Production Framework for Adaptive GPU Resource Management in Deep Learning Inference

Deploying AI models in production requires balancing throughput, latency, and infrastructure cost. Existing inference systems often rely on static GPU allocation and fixed batching strategies, leading to underutilized resources during low traffic and degraded performance during demand spikes. This paper presents a practical framework for adaptive AI inference that dynamically adjusts batching and GPU allocation based on real-time workload patterns. We implement the system using commodity GPU instances and evaluate it with vision and language models under varying traffic conditions. Results from experiments on NVIDIA A4000 and A5000 GPUs show throughput improvements of 1.4× to 2.1× compared to baseline configurations, with latency reductions of 18–32% during steady-state operation and 25–38% better GPU utilization. An ablation study confirms that adaptive batching and elastic GPU scheduling are complementary: neither mechanism alone captures the full benefit of their joint operation. The framework is designed for straightforward deployment in existing cloud environments without requiring specialized hardware or extensive reconfiguration.