Vultr and AMD Support the University of Cambridge’s TESSERA AI Project to Accelerate Global Environmental Monitoring

This section is Partnership Content suppliedThe content in this section is supplied by Business Wire for the purposes of distributing press releases on behalf of its clients. Postmedia has not reviewed the content. by Business Wire Article contentCambridge University’s Energy & Environment Group Deploys Planetary-Scale Foundation Model on Vultr Cloud GPU Infrastructure Powered by AMD Instinct™ MI325X GPUsSign In or Create an AccountEmail AddressContinueor View more offersArticle contentCAMBRIDGE, England — Vultr, the world’s largest privately-held cloud infrastructure company, and AMD, a leader in high-performance and AI computing, have announced their support for the University of Cambridge’s Energy & Environment Group in developing TESSERA, a groundbreaking AI foundation model designed to monitor environmental change internationally at unprecedented scale.

Article contentWe apologize, but this video has failed to load.Try refreshing your browser, ortap here to see other videos from our team.Article contentArticle contentVultr is providing the computational infrastructure, based on AMD Instinct MI325X accelerators, necessary to generate global embeddings covering Earth’s land surface from 2017 to 2025, enabling applications in agriculture, biodiversity conservation, and renewable energy infrastructure monitoring.

The collaboration represents a significant advancement in applying artificial intelligence to planetary conservation challenges.Article contentTop StoriesGet the latest headlines, breaking news and columns.There was an error, please provide a valid email address.

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Please try againInterested in more newsletters? Browse here.Article contentTESSERA processes data from European Space Agency Sentinel satellites to create detailed embeddings – compressed AI representations – of every 10-meter square of Earth’s surface.

TESSERA represents the most accurate pixelwise foundation model ever created from Sentinel satellite data, and leads the field in results and reproducibility. These embeddings enable researchers, governments, and organizations worldwide to track environmental changes, from crop health monitoring to deforestation detection, at a scale and accuracy previously unattainable.Article content“This collaboration positions Vultr at the intersection of cutting-edge AI infrastructure and planetary conservation,” said Kevin Cochrane, Chief Marketing Officer at Vultr.

“By providing the computational resources required for TESSERA and embracing open source technology, Vultr is enabling research that has immediate real-world impact, from helping smallholder farmers optimize crop yields to tracking biodiversity loss in critical ecosystems.”Article contentStephanie Dismore, Senior Vice President, EMEA at AMD added, “TESSERA shows what becomes possible when world-class research teams can scale AI with the right compute and an open software foundation. By running on Vultr cloud infrastructure powered by AMD Instinct™ MI325X GPUs and the AMD ROCm™ open software stack, University of Cambridge researchers can efficiently process petabytes of satellite data and generate planetary-scale embeddings that help turn environmental observation into actionable insight.”

Article contentThe project will leverage Vultr’s AMD-powered infrastructure, including 4×8 MI325X GPU configurations, to process and host the embeddings. This infrastructure will support both the intensive training requirements and long-term storage needs of the global dataset.Article contentProfessor Anil MadhavapeddyArticle content, Professor of Planetary Computing at Cambridge’s Department of Computer Science & Technology and Co-Director of the Cambridge Centre for Earth Observation, emphasized the project’s commitment to open access: “Our goal is to democratize access to planetary-scale environmental monitoring.

By making TESSERA’s embeddings freely available under a CC-BY license and publishing the complete training pipeline, we’re ensuring that any researcher, government, or organization worldwide can deploy this technology for their specific conservation needs.”Article contentArticle contentThe Energy & Environment Group will release not only the embeddings but also a fully reproducible pipeline for training the model from scratch on Vultr infrastructure, enabling deployment and fine-tuning for region-specific applications.Article contentThe project’s computational requirements include both GPU resources for model training and CPU capacity for processing raw satellite observations.

Article contentThree Key Application VerticalsArticle contentThe collaboration will develop case studies across three primary verticals:Article contentAgriculture & Farming Optimization