Understanding and comparing PixInsight Benchmarks

From reviews I have seen on the X3D feature, it is basically just additional cache on-chip which can assist "small" CPU workloads which require frequent cache transfers quickly - this benefits (some) games which rapidly load/unload relatively small bits of memory but is not ideal for large dataset workloads like PixInsight.  
For software like PixInsight, where you may be processing 100's of GBs of data between CPU, RAM, and SSD (e.g. when stacking), the X3D stuff doesn't really assist much. I've also seen reviewers mention the algorithm that controls the X3D cache sometimes does wonky things especially in benchmarks, sometimes choosing the wrong (slower) cache and actually hurting results.   
The things that benefit PixInsight the most: A very fast (and large capacity) SSD, as many CPU cores as practical (hence why the 5900X, 5950X, and 7950X benchmark so well), and a reasonable amount of high speed RAM (DDR4 or DDR5, minimum of 32-64GB). For processes such as RC Astro's plugins (BlurXterminator, StarXterminator etc) and starnet 2 which benefit from CUDA acceleration, get a decent GPU like an RTX 3080, 3090, 4080, 4090 etc. 

I would honestly stick with the 7950X or even save some money and get the 5950X.   

For what it's worth, I run a 2TB 980 Pro M.2 SSD, 5900X with 64GB of Corsair DDR4 3600MHz RAM, and a RTX 3090 with the CUDA acceleration stuff set up to improve CUDA accelerated processes in PixInsight. This is already "not the fastest configuration" with the new 7950X and 6000MHz DDR5 RAM now on the market, however, I do not feel like I need anything faster at the moment. This system is plenty fast enough for me.   

Some of the benchmarks you are seeing in the PixInsight benchmarks that use large amounts of RAM are configuring it as a RAM disk - I personally do not see the need to do this as M.2 storage has become so fast now and is cheaper than RAM per GB. 

Also, look into adding multiple temp file SWAP directories - I added 8x duplicate temp SWAP directories and improved my benchmark scores significantly.

I have a 5950x with 128GB RAM and a 3070 GPU and I run M.2 disks as well. I’m not sure what the practical limits are but at least the amount of memory isn’t that. The GPU operations are of course heavy and it feels like adding a faster GPU would improve things, but going from 20s to 10s per operation would have quite high price tag. I’ve seen my memory usage never peak above 64GB, so I’d look more at the speed and latency of the memory than the amount beyond that. Stacking and preprocessing is both CPU and I/O intensive so fast disk and a lot of cores rules once you have maybe more than 32GB of memory.

https://pixinsight.com/forum/index.php?threads/experimental-tensorflow-gpu-acceleration-repository.22325/

If you have a Nvidia GPU that is relatively new, you may want to check this out. It massively speeds up processes like StarNet, NoiseX or BlurX.

I had a GTX 1660 super and found the acceleration of the RC tools over just the cpu was great for normal sized files.  I then got a RTX 3070 on the used market and it was like flipping switch.  I can use it to accelerate in decent time very large images like mosaics.  Just my two cents but you really dont want to build a great PC with top tier cpu and ram then throw a GPU in that someone would probably not even use for a display output on a server.

The PI benchmark is not very memory intensive. So no challenge to RAM amount, but to RAM speed.Systems with more than two modules (typically 128GB with 4 modules) run on lower RAM speed and latency. You lose easily 10% or more in memory transfer and this penalty you will notice/measure everywhere.

You really should measure, whether you need more than 96 GB RAM (biggest amount possible with 2 DDR 5 modules). To buy 128 GB because you think you might need it, may lead quite to opposite effect. 
I have a fullframe ASI, and never needed more than 64GB. 

CS
Rüdiger

PS. Looking at the task manager is not sufficient. A smart operating system always tries to make use of ALL memory. E.g. for disk caching. So to determine the actual need, is a bit more sophisticated. A good guess is also having a look on the amount of images you have in a typical stack and whether you work with drizzling. Based on that you can calculate an educated guess.

I run the 7950X with 64gb of ddr5 memory.  I looked into getting 128gb, but the speed of ddr5 memory vs ddr4 led me to think that 64gb would be enough and it has proven to be true.  There's also a timing issue with ddr5 memory if you're using 4 cards vs 2, so I went with two 32gb ddr5 cards. cheers