Sustainable AI

Innovating smaller, faster, and more sustainable AI models and practices

• Energy-efficient model architectures and training methods
• Model compression and knowledge distillation
• Smaller and faster AI models for production deployment
• Responsible and environmentally conscious AI development
• Research partnerships advancing sustainable AI innovation

Partnerships

• York University academic 2026–2027

  Two-year academic research collaboration on AI applications

  • York Prof Innovating Smaller, Faster and More Sustainable AI

Open Source

models-eval-playbook

active

CLI-based playbook for evaluating LLMs with MLflow logging. Supports multiple providers including Azure OpenAI, Ollama, OpenRouter, and Alibaba Cloud. Enables testing a model under evaluation against a configurable judge model.