Artificial intelligence has experienced exponential growth in recent years, finding its place in various fields such as product recommendations, fraud detection, content generation, medical diagnostics, and many more. However, this rapid expansion comes with environmental consequences. Training and deploying increasingly complex models require substantial computing resources (GPU, TPU), leading to a rise in energy consumption and, consequently, a significant carbon footprint. With the rise of AI, it is estimated that the electricity consumption of a data center, which used to double every five years, will now double every year.

In response to these challenges, Frugal AI is emerging as a strategic solution aimed at optimizing resource usage, minimizing environmental impact, reducing costs, and maintaining satisfactory performance. One of the key approaches driving this trend is the development of Small Language Models (SLMs), which perfectly embody this new mindset. These models, more targeted and lightweight than large, general-purpose models, address a critical issue related to the environmental impact of AI development. In fact, the design phase alone is estimated to account for 70% of AI’s ecological footprint.

The foundations of frugal AI 

Adopting a sustainable and efficient approach to artificial intelligence relies on several key pillars.

The first pillar is based on what fuels AI: data. It is essential to validate the quality and relevance of the data in relation to the model. Managing and optimizing data storage becomes crucial in response to the explosion of generated volumes, encouraging selective sorting, compression, and the deletion of irrelevant data to reduce costs and energy consumption. Additionally, relying on open-source datasets for prototyping aligns with a resource-efficient approach.

Processing efficiency also plays a key role, with the optimization of algorithms, architectures, and training strategies through techniques such as pruning, weight quantization, and the use of simpler yet high-performing models.

Continuous optimization is an integral part of the eco-design approach. Regular audits and ongoing monitoring of resources (CPU, GPU, RAM, storage) help identify bottlenecks, inefficiencies, and areas for improvement. This ensures that development cycles follow a process of continuous improvement, aiming to reduce energy consumption.

Finally, cloud infrastructure can be a powerful lever by selecting providers that adopt green energy policies, with data centers powered by renewable energy and certified with environmental standards. A responsible development approach prioritizes models tailored to real needs, avoiding generic approaches and favoring more compact and specialized solutions that align with specific use cases and business constraints.

Small Language Models (SLM), icons of frugal AI

Small Language Models (SLMs) offer a more efficient and targeted approach to artificial intelligence, relying on several fundamental principles.

Unlike massive models (such as GPT-4 or Gemini 2.0) that require vast text corpora, SLMs operate with smaller, carefully selected datasets, significantly reducing energy consumption and training time. Another major advantage is their ability to be deployed locally: these models can run on low-power devices such as smartphones, IoT microcontrollers, or small shared servers, enabling embedded or offline AI services that save bandwidth while enhancing resilience. Moreover, SLMs stand out for their industry-specific specialization, whether in healthcare, finance, or manufacturing. This sectoral adaptation reduces model size and training resource requirements, ensuring optimal performance for specific use cases without the need for heavy, energy-intensive infrastructure.

The advantages of frugal AI

Frugal AI represents an innovative and responsible approach, offering numerous economic, ethical, and strategic advantages. By reducing the need for GPU resources, storage space, and computation time, it significantly lowers operational costs, making AI projects more financially accessible. This technological efficiency also enhances accessibility, enabling AI deployment on low-power devices or in low-connectivity areas, opening up new opportunities for affordable mobile solutions. Moreover, by embracing the challenge of "doing more with less," technical teams are encouraged to innovate, design smarter architectures, and explore optimization algorithms, fostering the emergence of high-performance and resource-efficient solutions.

Finally, adopting sustainable practices enhances a company's brand image, aligning with its CSR commitments and efforts to combat climate change.

How to implement frugal AI in your organization? 

Switching to Frugal AI doesn’t happen overnight, but it can be facilitated through a structured approach and adapted practices.Transforming stakeholders' mindsets is essential, encouraging a critical evaluation of actual needs—always with a focus on simplicity. Establishing dedicated governance helps structure this transition.

To adopt a frugal AI approach, start by: 

• Start small by targeting high-value use cases that do not require massive models, such as an internal chatbot, document classification, or a localized recommendation system.
• Work on your data by defining a lifecycle and optimizing it at each stage through cleaning, normalizing, archiving, or deleting unnecessary data.
• Test and evaluate—performance indicators should track the efficiency of the service by measuring energy consumption, training time, inference latency, and the overall cost of the AI system. These metrics help adjust strategies and quantify real benefits. The AFNOR SPEC 2314 document – a general framework for Frugal AI – can be applied as an environmental assessment methodology. Additionally, sharing environmental impact indicators raises awareness among stakeholders about the challenges involved.
• Train and raise awareness among teams by explaining the environmental and economic challenges—this shared understanding encourages responsible long-term technological choices.
• Collaborate with AI experts! Data engineering, MLOps, and cloud infrastructures are key assets—their technical expertise ensures that models, frameworks, and architectures are adapted to meet frugality objectives without compromising performance. Luckily, Smile has these capabilities and can provide you with the expertise you need.

Frugal AI and the future of Artificial Intelligence

As environmental concerns continue to grow, Frugal AI is emerging as a differentiation strategy and a way to anticipate future challenges. Regulations may evolve towards stricter environmental standards. A shift is already visible with the new European directive, CSRD (Corporate Sustainability Reporting Directive), which demands greater transparency and corporate commitment to sustainability across three key areas: Environment, Social, and Governance (ESG).

Customers, increasingly aware of the digital carbon footprint, may favor sustainable AI solution providers. Frugal AI thus paves the way for a new era, where technological innovation aligns with social responsibility and energy efficiency.

Frugal AI is not just a passing trend but a paradigm shift. It proves that efficiency, accessibility, environmental responsibility, and value creation can go hand in hand. By adopting this approach, you ensure the sustainability of your AI initiatives, strengthen your brand image, and prepare your organization for the climatic, economic, and regulatory challenges of the coming years.

At Smile, our AI Factory is dedicated to designing and developing tailor-made AI models that combine performance and sustainability. We help you adopt Frugal AI by selecting the most suitable technologies, architectures, and models for your specific needs while reducing your environmental impact. Discover how we put our vision into action on our AI Factory page.

Ready to take the leap? Contact us now to explore responsible AI solutions and make energy efficiency a driving force in your digital transformation. By embracing Frugal AI, you are laying the foundation for a greener, fairer, and more sustainable artificial intelligence—benefiting both your business and the planet.

To learn more, check out our white paper: "24 tips on how to frame all of your Generative AI projects".