5 Ways to Reduce Your AI Carbon Footprint

Sustainability in the AI era doesn't mean going back to pen and paper. It means optimizing our workflows to get the same results with less compute. Here are five practical strategies you can start today.

1. Batch Your Prompts

LLMs have a "context window" that they must process every time you send a message. If you say "Hi", they process previous messages. If you then say "Edit this", they re-process everything.

Tip: Write comprehensive prompts. Instead of 10 short back-and-forth messages, try to give context, instructions, and data in a single, well-structured prompt. This saves redundant processing.

2. Right-Size Your Model

Not every task needs a PhD-level intelligence. Using GPT-4 or Claude Opus for summarizing a simple email is like driving a Ferrari to the mailbox.

• Complex Reasoning: Use GPT-4, Claude 3 Opus.
• Drafting & Summary: Use GPT-3.5, Llama-70b, or Haiku.
• Formatting: Use tiny local models.

3. Cache Your Results

If you are a developer integrating AI, caching is non-negotiable. If your app asks the AI to "Generating a welcome message" for every user, you are wasting energy.

Generate it once, save it to a database, and serve the saved text. Only use the AI when the content specifically needs to be unique.

4. Run Local Models

Tools like LM Studio, Ollama, and Apple's MLX allow you to run powerful models (like Llama 3 or Mistral) directly on your laptop.

This removes the need for data center transmission and utilizes the efficient silicon already in your machine. It's often faster, more private, and greener for small tasks.

5. Measure Your Impact

You can't manage what you don't measure. Use tools like our AI Impact Calculator to visualize the real-world cost of your daily workflow.

Seeing your usage in terms of "LED lightbulb hours" or "Car miles driven" can be a powerful motivator to optimize your habits.