Amazon funds research making AI more efficient

A new investment in efficient AI

Amazon is providing research support to UC Merced to develop techniques that make artificial intelligence systems more efficient. The effort centers on reducing the computational and energy demands of training and running modern AI models, addressing a pressing challenge as systems grow in size and cost. The funding underscores a broader push across industry and academia to make AI not only more capable, but also more sustainable and accessible.

What the funding enables

With Amazon’s backing, UC Merced researchers and students will pursue approaches that cut the time, energy, and expense associated with AI workloads. That work typically spans:

Algorithmic improvements that speed up learning and inference
Model compression and pruning to shrink computational footprints
Hardware-aware scheduling that better utilizes accelerators
Data efficiency techniques that reduce redundancy without sacrificing accuracy

Together, these strategies can lower cloud costs, decrease latency for real-time applications, and reduce the carbon impacts of large-scale training.

Key players and collaboration

The collaboration brings together UC Merced faculty and student researchers with Amazon’s applied science perspective. By pairing academic exploration with real-world constraints drawn from Amazon-scale systems, the project is positioned to test ideas on realistic workloads in areas such as natural language processing, computer vision, and recommendation systems.

Why this matters for AI and technology

Efficiency is quickly becoming a foundational concern in AI:

Scaling models has driven up compute budgets, putting advanced AI out of reach for many labs and startups.
Energy use and emissions from large training runs have prompted calls for measurable reductions.
Practical deployments—from mobile and edge devices to latency-sensitive services—depend on models that are both fast and frugal.

Advances from this work can help democratize access to powerful AI, enable greener operations in the cloud, and open the door to more capable AI at the edge.

Central Valley significance

Located in California’s Central Valley, UC Merced serves a region better known for agriculture than for cutting-edge computing. This investment signals growing recognition of the campus as an emerging research hub and creates new training pathways for local students into high-impact tech fields. The work also complements Central Valley priorities—such as improving the efficiency of resource-intensive operations—by cultivating expertise that can transfer to regional sectors including ag-tech, logistics, and energy management.

Looking ahead

As the project progresses, expect an emphasis on reproducible measurement of efficiency gains, practical techniques that can be adopted by both industry and academia, and opportunities for students to engage with real-world AI systems. The partnership highlights a clear direction for the field: advancing AI capability while sharply improving the cost, speed, and sustainability of the underlying computation.

Central Valley AI is produced by the CVAI Education Desk team and developed by Kaweah Tech, a regional firm that builds, deploys, and integrates AI solutions for businesses across California's Central Valley.

Source

https://news.ucmerced.edu/news/2026/amazon-funds-research-making-ai-more-efficient