Optimizing AI Chatbot Energy Costs: Practical Metrics and Workflow Strategies
Optimizing AI Chatbot Energy Costs: Practical Metrics and Workflow Strategies Hook: “Training GPT-3 consumes ~1,300 MWh—but inference at scale can be worse. Here’s how to measure and slash your LLM’s energy footprint.” AI chatbots are electricity hogs. While training large language models (LLMs) like GPT-4 dominates sustainability discussions, inference—the process of generating responses to user queries—can cumulatively surpass training energy costs when deployed at scale. For DevOps teams and developers, unchecked energy use translates to: ...