Running large language models locally incurs electricity costs that are often underestimated. The primary factors determining these costs are the power draw of the GPU during generation, the electricity rate per kilowatt-hour, and the model's generation speed in tokens per second. While continuous generation on a high-end GPU like the RTX 4090 might cost around $0.072 per hour, the actual daily cost is significantly influenced by idle power consumption, which can outweigh generation costs at lower duty cycles. Additionally, cooling expenses, especially in air-conditioned environments, can add a substantial percentage to the overall electricity bill. AI
IMPACT Understanding the true electricity costs of local LLM inference is crucial for optimizing hardware usage and managing operational expenses.
RANK_REASON Article provides a technical breakdown and cost analysis for running local LLMs, focusing on hardware and electricity usage.
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