Machine learning, data scientist and real time professional simulations.

You need to have your models in video ram to be able to "play" with them in real time so with 20Gigs they can access larger models.

For Machine Learning (esp. computer vision) it is really important. First, as others have pointed out, it lets you use bigger batches of data in a single forward pass. This has not only performance aspects, but also certain regularization aspects. Second, if you want/need to use higher resolution inputs then you really need a lot of memory. For example, MaskRCNN - a popular instance segmentation network - can only do batch_size=1 at 960x960 pixel images (with some tweaks). 960x960 is not a particularity high resolution by contemporary standards and also a batch_size=1 can cause gradient fluctuations (this is usually not desired). There is also some trend in NLP (Natural Language) to work with lots of data/bigger models that are memory hungry (e.g. GPT-2).

Comparatively speaking Memory is cheap, I wish they started offering 32GB, 64GB or even 128GB for professional applications.

I could start playing around with GPGPU Database.

May not be required for gaming but for their GPGPU market that is going to be a big win.

Another use case is the Cloud gaming market where a single GPU needs to serve multiple users.

Absolutely. Being able to train with larger batch size helps a lot with performance

Need? Who knows. Do customers want it for various reasons? Absolutely.

4k and 8k gaming are definitely going to be a thing. Those textures are pretty big. (I think Final Fantasy XV is like 80GB larger if you get the 4k textures?)

YES! For rendering medium-large scenes in GPU based rendering engines such as Octane, Redshift, Arnold, etc.

Take a sportscar sample model with good resolution, say 250k polygons. If you want to create an animation with 100 cars crashing into each other, you'll need a lot of GPU RAM.

I imagine at least a part of that is because gamers want to see new hardware have bigger numbers than the old hardware and RAM is a pretty easy metric. RTX 2080s have 8-11 GB.

The cuckatoo_mean_cuda_rtx_32 solver for Grin's Cuckatoo32 proof-of-work needs that much memory. This PoW is actually intended for ASICs, that would need only 512MB of SRAM with a different approach, but those ASICs have yet to appear.

In the future, yes. As screen resolution increases a lot of textures increase as well. These mostly does not scale linearly as well so a large increase is to be expected from higher resolutions.

Google recently released their Performer architecture paper where one of the major achievements was making it work within 16GB. More headroom is incredibly useful!

It is common in VFX these days to have scenes that take well over 128Gb of RAM to render, 20Gb is, unfortunately, still quite thin :)

Maybe for real-time previews for video compositing such as Adobe After Effects?

3d artist here, yes Renderer can use your GPU RAM while rendering.