Evaluating the Impact of Collocating Deep Learning Tasks on DGX Spark

This project investigates how running multiple deep learning tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA DGX Spark. Students will deploy and benchmark various model in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between throughput, fairness, and system efficiency.

This project would be suitable as a BSc or MSc thesis. The workload can be adjusted based on the thesis and group size.