Verde

Inspiration

The growing energy demands of AI and data centers are among the most pressing sustainability challenges of our time. Currently, data centers account for 3.5% of U.S. electricity consumption, and this is expected to exceed 9% by 2030. As LLMs reshape how we learn and solve problems, their energy footprint demands action. We were inspired to create a more energy-efficient LLM, grounded in the belief that every choice has a ripple effect—that small, thoughtful changes can collectively create monumental impact. By making sustainability seamless, we empower everyone to play a role in protecting our planet. Every search, every action, every decision contributes to a greener, more sustainable future.

What it does

Verde is a cutting-edge inference engine that reimagines the way large language models (LLMs) operate. By integrating advanced techniques such as quantization, pruning, and parallel vector search, Verde achieves the following:

• Energy Efficiency: Reduces energy consumption by up to 60% per query compared to traditional LLMs.
• Carbon Emission Reduction: Significantly lowers the carbon footprint of LLM inference, contributing to global sustainability goals.
• User Analytics: Provides real-time feedback to users, showing them how much energy and emissions they’ve saved over their lifetime using Verde.
• Scalable Performance: Ensures faster, cost-effective AI deployments without sacrificing accuracy or functionality. Verde empowers individuals, businesses, and industries to adopt AI while reducing their environmental impact.

How we built it

We used an open-source LLM as the foundation for Verde and enhanced its inference engine with the following optimizations:

• Quantization: Converted high-precision model weights to lower precision without compromising accuracy.
• Pruning: Removed redundant parameters and layers to streamline the model for faster, lightweight performance.
• Parallel Vector Search: Implemented efficient indexing and retrieval techniques to minimize latency and energy usage.
• Energy Analytics Module: Built a dashboard to track and visualize energy savings and environmental impact in real-time. The infrastructure was designed to be cloud-compatible, ensuring Verde could be deployed seamlessly across diverse environments.

Challenges we ran into

• Balancing Efficiency and Accuracy: Achieving significant energy savings while maintaining model accuracy required rigorous testing and fine-tuning.
• Scaling Parallel Search: Ensuring that parallel vector search maintained performance across distributed systems was a technical hurdle.
• User Education: Integrating analytics in a way that informs users without overwhelming them posed a design challenge. Despite these challenges, we overcame them through iterative development and close collaboration among team members.

Accomplishments that we're proud of

• 60% Energy Savings Per Query: Demonstrated Verde’s ability to drastically reduce energy consumption while maintaining performance.
• User Analytics Platform: Created an intuitive tool that empowers users to see their individual and collective impact on sustainability.
• Proof of Scalability: Successfully tested Verde across various deployment scenarios, proving its adaptability for enterprise use. These milestones reinforce our belief in Verde’s potential to redefine how AI aligns with environmental goals.

What we learned

• The trade-offs between performance and energy efficiency require careful calibration and innovative solutions.
• User engagement increases significantly when they can see the tangible impact of their actions on sustainability.
• Collaboration across disciplines—AI, sustainability, and user experience—can lead to truly transformative products. These insights will guide our future developments and partnerships.

What's next for Verde

Verde is just the beginning of a movement towards sustainable AI. Our next steps include:

1. Expanding Use Cases: Integrating Verde into industries like healthcare, education, and customer service to maximize its impact.
2. Enterprise Partnerships: Collaborating with businesses to deploy Verde at scale, reducing operational costs and environmental footprints.
3. Global Awareness Campaign: Educating users and organizations about the environmental impacts of AI and the role Verde can play in mitigation.
4. Continuous Optimization: Further refining Verde’s algorithms to enhance energy efficiency and performance. We envision a world where AI innovation doesn’t come at the cost of our planet, and Verde is leading the charge towards that future.

Built With

• llm
• nextjs
• pinata