1. Even if your DL model is running on GPUs, you'll run into things that are CPU bound. You'll even run into things that are not multithreaded and are CPU bound. It's valuable to get a CPU that has good single-core performance.
2. For DL applications, NVMe is overkill. Your models are not going to be able to saturate a SATA SSD, and with the money you save, you can get a bigger one, and/or a spinning drive to go with it. You'll quickly find yourself running out of space with a 1TB drive.
3. 64GB of RAM is overkill for a single GPU server. RAM has gone up a lot in price, and you can get by with 32 without issue, especially if you have less than 4 GPUs.
4. The case, power supply, and motherboard, and RAM are all a lot more expensive for a properly configured 4 GPU system. It makes no sense to buy all of this supporting hardware and then only buy one GPU. Buy a smaller PSU, less RAM, a smaller case, and buy two GPUs from the outset.
5. Get a fast internet connection. You'll be downloading big datasets, and it is frustrating to wait half a day while something downloads and you can get started.
6. Don't underestimate the time it will take to get all of this working. Between the physical assembly, getting Linux installed, and the numerous inevitable problems you'll run into, budget several days to a week to be training a model.