Really depends on what you want to be doing with it, even in terms of QuPath projects, but some thoughts:
NVMe primary hard drive. The speed increase is worth it. I generally use something like the 970 EVO.
Large solid state hard drives for storage. I just RAIDed two 1TB HDDs for a project with 500 whole slide images. You will get much better performance than plugging in external hard drives or working off of a network/cloud. If you will also be handling large 3D data sets, say lightsheet, you may want to go much larger (8TB or so).
There is generally a tradeoff between single core speeds and having a lot of cores… unless you start spending a lot of money. Some processes will only ever run on a single core, and so a 32 core multi-cpu monster might end up being slower in some cases than a basic desktop. More cores allows anything tiled too be run more quickly though, N tiles can be processed at once. Great for large numbers of cells. If you want both high individual process speeds and high multi-core speeds, the CPUs can start costing tens of thousands of dollars.
On the other hand, the pixel classifier is huuuuungry, and I have run into a few cases of it simply closing down QuPath with no warning even with 25GB of RAM available (of 32). It seems like total CPU throughput and having plenty of memory is key to using that efficiently. I would recommend 64 GB of RAM, especially if you want to run more than one instance of QuPath at a time.
With 200+GB of RAM I was able to run 4 projects pixel classifying 10 images each all at once without problems, but dipping below 25GB of RAM per instance started causing me problems. Not sure if that will be an issue for you.
If you want to do other kinds of image analysis, say 3D analysis using other software, you will likely want to consider a nice video card as well. In the case QuPath allows you to train deep learning models directly in the future, an Nvidia card with plenty of RAM will definitely come in handy. The newer RTX cards have some nice improvements for deep learning applications. I haven’t used it, but there is also a CLIJ plugin for QuPath created by @haesleinhuepf that might allow you to improve processing speeds using the GPU, but that is not a default part of QuPath, and I’m not sure how many versions of QuPath it is compatible with.
Obviously not all of this is needed to simply run QuPath, but depending on how much you plan to push the limits… Anyway, I never like using pre-built systems so I don’t have any recommendations there.