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LocalLite

Runs AI inference locally by optimising GPU memory, letting organisations deploy multi-agent systems without cloud API costs or data leaving the building.

Institution
Trinity College Dublin
LocalLite

About the project

The rapid adoption of agentic AI is driving unprecedented demand for scalable inference infrastructure, yet most organisations remain dependent on cloud-based AI services that incur high API costs, introduce latency and create significant data privacy and governance challenges. These limitations are particularly acute for multi-agent AI systems, where operational costs increase rapidly with scale, restricting wider adoption across industry.

LocalLite addresses this challenge through a software platform that enables efficient, high-performance local AI inference by optimising GPU memory utilisation. By maximising the capacity of existing hardware, LocalLite allows organisations to deploy sophisticated multi-agent AI systems on-premises, dramatically reducing reliance on expensive cloud services while improving performance, security and responsiveness. The platform is designed to deliver up to a 90% reduction in AI operating costs, alongside lower latency and higher throughput, without requiring additional GPU infrastructure.

Last updated: 8 September 2026

Project team

Dr. Mohammed Rameez Qureshi

Lead researcher
Trinity College Dublin

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