Which solution will meet these requirements?

Explanations:

Switching to an instance type that has only CPUs would eliminate the GPU resources entirely. This would likely increase training times significantly, as deep learning models benefit from GPU acceleration for training tasks. Therefore, this option would not reduce costs effectively without extending the duration of the training jobs.

Using a heterogeneous cluster with two different instance groups could potentially optimize resource usage, but it may not specifically address the issue of GPU idleness. The complexity of managing different instance types could introduce inefficiencies and would not necessarily guarantee cost reduction without increasing training time.

Using memory-optimized EC2 Spot Instances might reduce costs but does not directly address the idle GPU issue. While Spot Instances can provide lower pricing, the focus is on memory optimization rather than optimizing CPUusage, and they can also be interrupted, which could lead to longer training times.

Switching to an instance type with a CPUratio of 6:1 would increase the number of GPUs relative to CPUs, making better use of the GPU resources. This would reduce the GPU idle time and potentially decrease training costs without increasing the duration of the training jobs, as the model can be trained more efficiently.