AI and climate: towards more responsible AI

Artificial intelligence is transforming our businesses, industries, and everyday practices. Yet behind the promises of performance lies a frequently underestimated climate impact. Training and running AI models require significant energy consumption, raising critical questions in an era where digital sustainability and efficiency are increasingly vital.

Responsible AI: climate urgency and digital sobriety

As AI usage explodes, its carbon footprint becomes a critical issue. The development of giant models, the rise of the cloud, and increasingly powerful infrastructures all significantly increase digital energy consumption.

In response, the concept of frugal AI is emerging. Defined by AFNOR, frugal AI is artificial intelligence that limits resource consumption throughout its entire lifecycle — from design to deployment. It is also a key pillar of digital sobriety.

Training vs inference: understanding the real energy costs of AI

To accurately measure the impact of an AI business solution, it is important to distinguish two key phases:

Model training: this initial phase often takes place on thousands of GPUs in data centers over several days or weeks. It is extremely energy-intensive. For example, training a large language model can produce as much CO₂ as a transatlantic flight.
Inference: this is the phase where the model is used (to generate text, analyze images, etc.). Less energy-consuming, it becomes significant at large scale, especially in public cloud services.

Reducing the climate impact of AI therefore involves technical choices across both phases, but most importantly in everyday usage.

Bigger ≠ better: model size directly impacts the climate

The most powerful AI models today (like GPT-4 or LLaMA 3) are also the heaviest, with several hundred billion parameters. The larger the model, the more expensive it is to train, store, and run.

The good news is that techniques now exist to reduce model size without sacrificing performance. The French government’s site Comparia.gouv.fr even provides detailed comparisons of the environmental impact of AI models.

This is the approach we take at Artemia.ai: using lighter AI models optimized for specific business use cases, delivering practical performance with significantly less climate impact.

Server location: towards truly local and efficient AI

The location of servers hosting AI models plays an important role in their energy impact.

Models run on international clouds involve frequent and energy-costly data transfers.
Moreover, these solutions raise concerns about confidentiality, digital sovereignty, and GDPR compliance.

Conversely, local AI solutions deployed on-premise or on European cloud infrastructures limit energy consumption related to data transfer. They also provide full control over data flows, security, and performance.

Artemia.ai: a local, frugal, and cloud-free generative AI

At Artemia.ai, we have chosen a secure, efficient, and frugal generative AI:

Our AI software for business is designed to run locally, on-premise, as close as possible to the end user.
Our models are optimized for practical performance, with reduced sizes tailored to each use case.
The recommended hardware to run our models consumes 5 to 10 times less energy than the massive GPU infrastructures of public clouds.
The result: lower consumption, fewer emissions, and greater control.

Our cloud-free AI software offers businesses a powerful alternative that is environmentally friendly and compliant with security and confidentiality requirements.

Conclusion

Artificial intelligence has a crucial role to play in transforming businesses. However, it must be designed responsibly and with efficiency in mind.
By choosing a local, frugal, and secure AI solution like the one offered by Artemia.ai, businesses commit to a sustainable digital transition that combines innovation with respect for the climate.