Description
Memory optimization can unlock bigger AI models on lower-cost NVIDIA Jetson SKUs. By reducing software overhead, choosing efficient inference runtimes, and quantizing models, developers can make room for more AI - and reduce hardware costs.
In this livestream, NVIDIA partners RidgeRun and Connect Tech will share demos and lessons learned on memory optimization, covering model quantization, efficient inference runtimes, and NVIDIA Jetson Agent Skills. You’ll see how large vision-language models fit into less memory and how a real robotics application moved to a more cost-efficient Jetson SKU.
You’ll learn:
- How much cost savings memory optimization can unlock.
- How to apply quantization, runtime selection, and NVIDIA Jetson Agent Skills to fit bigger models into less memory.
Join us live, bring your questions, and learn how to fit bigger models into smaller memory while reducing edge AI deployment costs