Introduction
Generative AI tools like ChatGPT have revolutionised how individuals and organisations create content, solve problems, and automate tasks. Yet behind every conversational exchange with the chatbot lies a significant environmental footprint. As demand for large language models surges, so too does the electricity required to power the data centres, cooling systems and networking infrastructure that make real-time AI possible. A recent analysis by Verdict has identified the ChatGPT prompts that inflict the heaviest toll on the planet. Understanding which queries consume the most energy—and why—can help users strike a balance between harnessing AI’s potential and curbing its carbon emissions.
1. The Environmental Toll of AI Inference
1.1 Data centres and energy consumption
• Modern AI relies on clusters of graphics processing units (GPUs) specially optimised for neural-network calculations.
• Estimates suggest that each GPU can draw between 300 and 600 watts under full load—comparable to a small electric heater.
• Cooling those GPUs often doubles the energy requirement, pushing the total demand to roughly 1,000 watts per rack.
1.2 From training to inference
• Training state-of-the-art models like GPT-4 consumed hundreds of megawatt-hours and generated more than 500 tonnes of CO₂.
• Inference—the process of serving end-user requests—accounts for another significant chunk of energy, scaling with user demand.
• A single, complex ChatGPT query can consume as much energy as running a modern laptop for several hours.
1.3 Why prompt design matters
• Longer prompts and requests for lengthy responses translate directly into more neural-network layers being activated.
• Multi-step or chain-of-thought prompts further magnify GPU usage by triggering multiple forward passes.
• Embedding images, tables or code examples increases computational overhead: the model must process richer content.
2. The Top 5 Most Energy-Intensive ChatGPT Prompts
2.1 Full-length novel generation
• Description: “Write me a 60,000-word fantasy novel with character arcs, world-building and dialogue.”
• Impact: Generating tens of thousands of tokens in one go can consume up to 1.2 kWh—equivalent to boiling 12 kettles of water.
• Carbon footprint: Roughly 700 g CO₂ per request, similar to charging three smartphones five times.
2.2 Live lecture translation
• Description: “Translate this two-hour video lecture in real time into five languages, providing timestamps and speaker accents.”
• Impact: Streaming audio in, transcribing, translating and streaming out ties up GPUs for extended periods—up to 4 kWh.
• Carbon footprint: Nearly 2,400 g CO₂, comparable to driving a petrol car for 8 km.
2.3 Large-scale codebase refactoring
• Description: “Analyse my 200,000-line code repository, suggest performance improvements, and rewrite the modules accordingly.”
• Impact: Parsing, understanding and rewriting thousands of code files triggers multiple inference cycles, totalling around 0.9 kWh.
• Carbon footprint: Approximately 550 g CO₂—about the same as heating a small room for an hour.
2.4 Bulk data analysis and visualisation
• Description: “Examine this 10 GB dataset of customer transactions, identify patterns, create charts and propose A/B tests.”
• Impact: Embedding large datasets and generating visuals pushes GPU load to nearly 1.5 kWh in one sitting.
• Carbon footprint: Around 850 g CO₂, akin to a domestic dishwasher cycle.
2.5 Multi-modal image-enhanced requests
• Description: “Here are 50 high-resolution images of urban street scenes—classify objects, estimate population density and propose urban design changes.”
• Impact: GPT-4V’s image-processing layers are computationally heavy; one batch job can require 1.8 kWh or more.
• Carbon footprint: Up to 1,000 g CO₂, similar to a load of laundry.
3. Reducing Your AI Carbon Footprint
3.1 Optimize prompt length and complexity
• Be concise: Only include essential context.
• Chunk tasks: Break large requests into smaller, sequential queries to avoid single high-energy bursts.
3.2 Choose the right model
• Use smaller models (e.g. GPT-3.5) for straightforward tasks; reserve GPT-4 for mission-critical or highly nuanced work.
• Experiment with domain-specific or fine-tuned models, which often run on less power.
3.3 Schedule non-urgent queries off-peak
• Run big jobs during times when renewable energy supply is highest.
• If using a cloud provider, select regions powered predominantly by wind, solar or hydroelectricity.
3.4 Leverage local computation or co-processing
• For developers: use on-device or edge-AI solutions for smaller workloads.
• Hybrid approach: pre-process data locally, sending only essential fragments to the cloud.
Conclusion
AI’s exponential benefits cannot come at the expense of the environment. While ChatGPT and other large language models unlock new frontiers in creativity, automation and accessibility, they also place considerable strain on our planet’s energy resources. By identifying the worst offenders—those sprawling prompts and data-heavy requests that drive up GPU usage—and adopting responsible usage strategies, users can harness the power of generative AI while keeping CO₂ emissions in check.
Three Key Takeaways
• Not all prompts are equal: requests that demand lengthy text, real-time translation or multi-modal processing consume far more energy and produce higher CO₂ emissions than concise queries.
• Prompt engineering matters: clear, targeted questions reduce unnecessary computation. Breaking large tasks into smaller steps can spread out energy use and lower peak demands.
• Sustainable AI choices: opt for smaller models when suitable, schedule intensive workloads during periods of high renewable energy availability, and consider on-device or edge-AI alternatives for routine operations.
Three-Question FAQ
1. Q: How much energy does an average ChatGPT prompt use?
A: A typical short conversational exchange may draw 0.05–0.15 kWh, roughly equivalent to charging a smartphone twice. In contrast, complex, multi-hour or multi-modal requests can range from 0.5 kWh up to 4 kWh per query.
2. Q: Are smaller AI models always more environmentally friendly?
A: Generally yes—models with fewer parameters require less compute per inference. However, accuracy trade-offs and task requirements should be considered. For highly detailed or nuanced tasks, a larger model may still be more efficient overall if it delivers correct results faster.
3. Q: Can I offset the carbon footprint of my AI usage?
A: Offsetting is possible through certified carbon-credit schemes, but reducing real consumption—through prompt optimisation, scheduling, and responsible model selection—is the most effective long-term strategy. Many cloud providers also offer carbon-neutral or renewable-powered compute options.
