Efficiency Is the Key to Sustainable AI

The recently announced strategic partnership between OpenAI and NVIDIA has spotlighted the issue of AI energy consumption. Under this deal, the companies will build and deploy more than 10 gigawatts of AI data centers, a scale so vast that their planned facilities could consume as much energy as the entire city of New York.

This staggering figure highlights the urgent challenge: current AI models demand enormous amounts of power, and this trajectory is unsustainable as AI adoption accelerates across society. Energy efficiency must become a first-class constraint in AI progress to ensure a sustainable AI future.

The Growing Debate: Energy Costs of AI Models

Big Tech is beginning to take this issue seriously. For example, Google recently published a paper measuring the environmental impact of delivering AI at scale with its Gemini series of models.

• The study found that the median Gemini Apps text prompt consumed 0.24 Wh of energy.
  
  
• By deploying more efficient software, Google achieved a 33× reduction in energy consumption and a 44× reduction in carbon footprint per prompt over one year.

This positions Google as a leader in sustainable AI development, demonstrating that efficiency at scale is possible and necessary.

Efficiency and Hardware: The Future of Sustainable AI

To achieve true sustainability, future AI systems must combine:

1. Efficient AI models – optimized through pruning, quantization, knowledge distillation, and architecture search.
   
   
2. Energy-efficient AI hardware – specialized platforms designed to minimize power consumption.

This is where Embedl’s award-winning technology comes in. Our SDK integrates state-of-the-art methods in model compression and optimization, ensuring that AI models run faster, leaner, and with lower energy demands.

Embedl’s Breakthrough in Small Language Models

The next wave of agentic AI technologies will be powered by Small Language Models (SLMs) such as Llama 3.2 and Gemma. However, these models still face efficiency challenges that drive significant compute and energy usage. Embedl’s new technology has achieved breakthrough results with SLMs:

• Up to 1.75× end-to-end speedups, on top of state-of-the-art optimizations such as FlashAttention, KV-Cache, and Quantization, even at the billion-parameter scale.
  
  
• Preserved accuracy, robust across Llama-3.2, Llama-3.1, Gemma-3, and Qwen-3 families.

This innovation makes SLMs dramatically more efficient, paving the way for scalable, sustainable agentic AI. Embedl will soon release these optimized models ready for deployment.

Toward an Energy Efficiency Rating for AI Models

Just as household devices like refrigerators and washing machines carry energy efficiency ratings, the future of AI could adopt similar systems. A recent Nature article even suggested such ratings for AI models.

At Embedl, we take this vision further. Through our SDK and Hub, we aim to provide fine-grained efficiency ratings tailored to specific consumer-grade hardware platforms. This would allow customers to choose AI solutions optimized for their devices, making the wide adoption of sustainable AI a practical reality.

Conclusion: Building a Sustainable AI Future

The path to sustainable AI is clear: efficiency must be at the center of innovation. As AI systems scale, the energy impact cannot be ignored. We can reduce power consumption by combining efficient software, specialized hardware, and technologies like Embedl’s without compromising performance.

At Embedl, we believe sustainable AI is not just possible but essential. By making energy efficiency a core priority, we can ensure AI continues to grow responsibly, benefiting society while protecting the planet.