Small changes in the large language models (LLMs) at the heart of AI applications can result in substantial energy savings, according to a report released by the United Nations Educational, Scientific and Cultural Organization (UNESCO) on Monday.

The 35-page report titled “Smarter, smaller, stronger: resource-efficient generative AI & the future of digital transformation” outlines three ways AI developers and users can reduce the power gluttony of the technology.

1. Use smaller models.

Smaller models are just as smart and accurate as large ones, according to the report. Small models tailored to specific tasks can cut energy use by up to 90%, the report maintained.

Currently, users rely on large, general-purpose models for all their needs, it explained. Research shows that using smaller models tailored to specific tasks — like translation or summarization — can cut energy use significantly without losing performance. It’s a smarter, more cost- and resource-efficient approach, it continued, matching the right model to the right job, rather than relying on one large, all-purpose system for everything.

What’s more, energy-efficient, small models are more accessible in low-resource environments with limited connectivity, offer faster response times, and are more cost-effective.

2. Use shorter prompts and responses.

Streamlining input queries and response lengths can reduce energy use by over 50%, the report noted. It added that shortening inputs and outputs also reduces the cost of running LLMs.

3. Use compression to shrink the size of the model.

Model compression techniques, such as quantization, can achieve energy savings of up to 44% by reducing computational complexity, the report explained. It also reduces the cost of running LLMs by shrinking their size and making them faster.