Large Language Models (LLMs) are extremely resource-intensive to deploy, demanding high memory and compute. Static provisioning often leads to waste or unmet demand. We propose a conceptual framework that uses reinforcement learning (RL) and self-adaptive software engineering to optimize resource use in LLM deployments. An RL agent monitors system metrics (throughput, latency, GPU/CPU utilization) and takes actions such as scaling instances, adjusting model precision, or modifying batch sizes. The system employs a Monitor-Analyze-Plan-Execute (MAPE-K) loop where dynamic configuration parameters are tuned online to maximize throughput and minimize cost. We illustrate the approach with examples: RL-driven autoscaling (showing ~40–50% higher GPU utilization) and adaptive inference optimizations like key-value caching (up to 4× speedup). Real-world LLM deployments (cloud services and edge settings) exhibit highly variable workloads; our framework adapts to these changes. Experiments and industry reports show that RL-based adaptation can significantly improve resource efficiency and performance.
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