Maximizing Memory Efficiency to Run Bigger Models on NVIDIA Jetson

The boom in open source generative AI models is pushing beyond data centers into machines operating in the physical world. Developers are eager to deploy these models at the edge, enabling physical AI agents and autonomous robots to automate heavy-duty tasks. A key challenge is efficiently running multi-billion-parameter models on edge devices with limited memory. With ongoing constraints on memory supply and rising costs, developers are focused on achieving more with less. The NVIDIA Jetson platform supports popular open models while delivering strong runtime performance and memory optimization at the edge. For edge developers, the memory footprint determines whether a system functions. Unlike cloud environments, edge devices operate under strict memory limits, with CPU and GPU sharing constrained resources. Inefficient memory use can lead to bottlenecks, latency spikes, or system failure. Meanwhile, modern edge applications often run multiple pipelines—such as…