AI Models Comparison: Examining the Environmental Footprint of LLMs and SLMs
In the ever-evolving world of AI, a new trend is emerging: Small Language Models (SLMs). These compact and optimized models, often under 10 billion parameters, are becoming a viable alternative to Large Language Models (LLMs) due to their environmental benefits and cost efficiency.
LLMs, such as Claude 3 and Llama 3, are massive AI systems with billions of parameters, trained on enormous datasets to handle complex tasks. However, their vast energy and resource demands have raised concerns about their environmental impact. A single LLM query emits 2-5 g of CO2e, compared to 0.1-0.5 g for SLMs.
On the other hand, SLMs, like Gemma (2-7B) or Mistral-7B, are designed to run efficiently on edge devices like smartphones, extending the life of hardware by running on older devices. They excel at specialized tasks while using a tenth of the computation of LLMs.
Companies like NVIDIA, Hugging Face, OpenAI, and Grok.AI are at the forefront of SLM development. NVIDIA introduced Nemotron-Nano-9B-V2, a small model optimized for edge and agent use. Hugging Face, in collaboration with over 1,000 researchers globally, created BLOOM, a large, multilingual model with 176 billion parameters. OpenAI developed ChatGPT as a powerful LLM, while Grok.AI is from X.ai, a startup founded by Elon Musk after splitting from OpenAI.
Cerence Inc. has developed both large and small language models (CaLLM™ family) integrated into automotive AI platforms, partnering with companies like Microsoft, NVIDIA, MediaTek, and SiMa.ai for advanced edge AI applications. The Chinese company DeepSeek released DeepSeek-LLM models with 7B and 67B parameters.
IDC (2025) projects that SLMs will capture approximately 40% of the AI market, especially in mobile and edge computing. On-device processing can reduce data transfer emissions by as much as 80%. Hybrid approaches that use SLMs for routine queries and LLMs for escalations can reduce emissions by ~70%.
Businesses should assess task complexity when deciding between SLMs and LLMs. SLMs are ideal for lightweight, efficient apps, while LLMs outperform in general-purpose intelligence and multimodal tasks. For instance, LLMs like Claude 3 demonstrate superiority in image-text reasoning.
Environmentalists, like Dr. Emily Greenfield, have emphasized the need for a more sustainable and responsible way to scale AI. Tools like CodeCarbon and MLCO2 calculators, now embedded in Hugging Face, help measure AI model emissions. Microsoft aims to become carbon-negative by 2030 and prioritizes SLM development.
California's AI Transparency Act (2025) requires companies to disclose AI emissions. As awareness grows, it is expected that SLMs will play a significant role in reducing the carbon footprint of AI. For instance, GPT-4's training generated nearly 5,184 tons of CO2e, equivalent to the annual emissions of 200 households in the United States.
However, it's not all doom and gloom for LLMs. Companies like Google are already leveraging them for sustainable purposes. Google's Gemma is powering agriculture apps to optimize water usage. Fine-tuned SLMs now achieve diagnostic accuracy rivaling LLMs with far less energy.
In conclusion, the shift towards SLMs is a step towards a more sustainable and cost-efficient future for AI. As more companies embrace this trend, we can expect to see a reduction in the carbon footprint of AI and a move towards more responsible AI development.
