That’s a harder question. 
Access to Power systems with GPU is available (for a fee) in the IBM PowerAI Cloud:
https://cloud.ibm.com/catalog/services/powerai 13
But those are POWER8 systems (with P100) and appear to still offer PowerAI 1.5.3 pre-installed, which is older than what you’d want.
If you’re working with a common model, or a variant of one, we might be able give you a ballpark of GPU memory usage in the non-LMS vs LMS case.
Or, the current release of PowerAI actually includes both Power and x86 packages in the conda channel. So you could A/B test for GPU memory usage LMS vs non- on an x86 machine. LMS will perform better on an AC922 due to the NVLINK connections providing higher bandwidth between GPU and host memory, but it works on x86 as well.
A few notes:

We’re preparing to update our conda channel for our upcoming 1.6.1 release, and that may take a day or two for that to settle. If you wanted to install today, you’d probably want to take care to restrict the intall to 1.6.0:

$ conda install -y -n my-pai-env python=3.6 pytorch powerai-release=1.6.0


An added caution for x86: Anaconda publishes a pytorch package in their free channel, and their installer sometimes prefers stuff from the free channel over higher priority channels. So if you go that route please see the Tip at the bottom of: https://www.ibm.com/support/knowledgecenter/SS5SF7_1.6.0/navigation/pai_setupAnaconda.html 6

We include the necessary CUDA, etc. packages in the conda channel, but you do need to download (from NVIDIA) and install the GPU driver separately. Our 1.6.0 and 1.6.1 releases are both built against CUDA 10.1 and so require the 418-series GPU driver.
The upcoming PowerAI / WML CE 1.6.1 PyTorch package is based on 1.1.0, and it includes an LMS update that should reduce GPU memory further in the default/conservative case.