Again this topic is covered in more detail here, but in short, for 3D visualisation we typically use the Graphics Card (GPU), and the GPU can be considered thought of as a computer within the computer.

The GPU has its own computing cores, its own clock rate, its own memory etc. The amount of data that a GPU can process is also limited by these factors. Again there are no consumer GPUs with anything close to 1TB of memory. Typically, GPUs come with 2-20GB of VideoRAM, with some high end server GPUs going up to 128GB of VRAM. However, being able to hold that much data in memory doesn't mean the GPU is capable of rendering these pixels on the screen in 3D in the time needed for an interactive visualisation.

For an interactive visualisation we need to render an image every 100ms as a bare minimum, though most users would consider 10fps laggy and prefer something closer to 30-60fps. At 60fps we only have around 16ms to calculate and display each image, and no GPU can get anywhere near to rendering more than around 2 gigapixels of 3D data within that time. Therefore, arivis will subsample the dataset into the GPU RAM and display the subsampled dataset as fast as possible.

Again, if we look in the Task Manager this will appear as if the software is only using 10-20% of the GPU resources, but using more would usually result in a noticeable drop in performance.