SAGEA for Learning
Frontier models on campus.
We are deploying our most capable models directly into academic institutions — giving students, researchers, and faculty access to the same systems that power our enterprise infrastructure.
We are expanding access to our research and models across Nepal’s universities and research institutions. SAGEA for Learning provides direct access to SAGE Actus and SAGE Celer — not simplified interfaces or API wrappers, but the actual models, running locally on institutional infrastructure. Through on-campus deployments and researcher-led workshops, we are creating environments where academic communities can engage with frontier AI as a tool for serious inquiry, experimentation, and long-term capacity building.
Latest highlights
SAGE Magnus Completes Phase 1 Pre-training: Architecture and Origins
Introducing SAGEA for Learning
Introducing the SAGEA Community
Learning to Read from Fragments: Scaling Devanagari OCR With GLUE Engine
Introducing SAGE-OSS-40B
Introducing Rune
Why expanding access to frontier AI matters
Science and engineering advance fastest when researchers have direct access to the most capable tools available. Today, most students and academics interact with AI through consumer interfaces — tools designed for general use, with limited visibility into the underlying systems.
SAGEA for Learning is designed to change this. By deploying our models on campus infrastructure, we give academic communities the ability to work with frontier systems directly — running experiments, building applications, and conducting research with the same models used in production deployments.
Built with academic communities, for academic communities
SAGEA for Learning is not a one-time demonstration. It is a sustained program designed to embed frontier AI into the academic workflow of partner institutions across Nepal.
By partnering directly with universities and running independent workshops, we are building long-term capacity rather than short-term exposure. Each deployment is designed to integrate into existing academic and research workflows, from illustrative exercises and proof-of-concept generation to scientific computing, benchmarking, and experimentation.