The following prompt was entered into ChatGPT asking about the energy consumed from processing a single ChatGPT question.
How much energy does a single chatgpt query use
The ChatGPT response was as follows:
The energy consumption of a single ChatGPT query can vary based on multiple factors, including model size, hardware efficiency, data center efficiency, and the length of the response generated. While precise figures for energy use per query aren’t typically shared publicly, estimates based on similar models provide some general insights.
Estimated Energy Usage for a Single ChatGPT Query
Using information from similar models and data centers, a single ChatGPT query has been estimated to consume around 0.1 to 1 watt-hour (Wh) of energy. Here’s a breakdown of what contributes to this range:
- Model Size: Larger models (like GPT-4) generally require more energy than smaller ones, as they have more parameters to process.
- Query Complexity: More complex or longer queries with extensive responses can increase computational demand.
- Data Center Efficiency: Energy efficiency varies widely across data centers, from older, less efficient ones to modern, highly optimized ones.
- Hardware: Optimized hardware like TPUs (Tensor Processing Units) or GPUs can process queries more efficiently, reducing energy per query.
Contextualizing the Energy Usage
For comparison:
- 0.1 Wh is roughly equivalent to keeping an LED light on for a minute or two.
- 1 Wh would be equivalent to using a typical LED light for about an hour.
These estimates provide a general sense of the energy impact per query, though actual energy use can vary depending on specific deployment factors and hardware setups.
This response was then followed by this prompt:
What were the sources for your response?
ChatGPT then responded as below:
The response was based on general industry knowledge and estimates from publicly available studies on the energy consumption of large language models and AI infrastructure. Here are the primary types of sources and studies that informed the response:
- Research Papers on Model Energy Consumption:
- Papers that analyze the energy and environmental impact of training and running large language models (e.g., studies by OpenAI, Google, Microsoft, and academic institutions) provide benchmarks for the power requirements of various AI tasks.
- For instance, “Energy and Policy Considerations for Deep Learning in NLP” (Strubell et al., 2019) explored energy usage for NLP models, helping establish an estimated range for inference costs.
- Data Center Efficiency Reports:
- Reports and white papers from data center providers and research organizations, like Google, Microsoft, and Amazon, highlight the range of energy efficiencies achieved in modern data centers, which affect the energy per query.
- Sources like Google’s AI and machine learning efficiency reports and the Uptime Institute’s data on PUE (Power Usage Effectiveness) provide context on the infrastructural efficiency supporting AI.
- Generalized Estimates from Industry Experts:
- Some AI experts and organizations (such as OpenAI, DeepMind, and AI researchers at MIT) have shared estimates and insights in blogs, interviews, and conferences about the energy impact of language models, particularly in recent years.
If you’d like precise figures or specific technical details, these sources can provide foundational insights, though specific numbers for individual queries may not be publicly available.
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