Story time: I went through this same nightmare last year. I thought I'd be smart and save cash with a standard build, but it was honestly a total mess for my bigger datasets. My experience trying to run 128GB of RAM on a regular board was just... not as good as expected. The memory controller kept failing during long Scikit-learn runs, which was literally the worst. Anyway, after basically wasting a month and feeling super disappointed with my first choice, I found these worked better:

• Intel Xeon W5-3435X Processor
• I finally switched to this and the stability is night and day for MKL tasks.
• Intel Xeon Gold 5415+ Processor
• It's slower on single threads but it's basically a tank for long simulations.
• Intel Xeon Silver 4410Y Processor
• Good if u need the lanes and stability on a tighter budget. So yeah, workstation chips are probably ur best bet if u want to avoid thermal issues. gl!

oh man, i feel u on this. I went through this last year when I thought I could just brute force a massive simulation on a standard consumer setup. I was running a huge ensemble model overnight on an i9 build with 128GB of non-ECC RAM. Honestly, it was a total disaster. The system seemed fine for an hour, but around 3 AM, the memory controller basically just couldn't handle the heat and the density. I woke up to a blue screen and lost about 14 hours of progress. It was sooo frustrating cuz the raw speed was there, but the reliability just... wasnt as good as expected. If ur doing professional work where time is money, i highkey recommend stepping up to the Intel Xeon W9-3475X Processor. While the i9-14900K mentioned earlier in the thread is fast, it lacks the safety features you need for massive datasets. This Xeon gives u 36 cores and way better thermal management for long-term endurance. Also, u gotta pair it with a solid board like the ASUS Pro WS W790E-SAGE SE WIFI Motherboard to actually support the registered ECC memory. Staying with the workstation class means u get ECC support, which is huge for avoiding those random bit-flips during 24-hour pandas operations. Unfortunately, the consumer chips just dont offer that peace of mind. Plus, the extra PCIe lanes from something like the Intel Xeon W9-3495X Processor are a lifesaver if u ever decide to add multiple GPUs for acceleration later. Just sharing my experience so u dont make the same mistake i did! gl!

In my experience, if youre already pushing 128GB of RAM and doing long, overnight training runs, the consumer-grade chips like the Intel Core i9-14900K Desktop Processor can be a bit of a headache. I mean, the raw speed is literally amazing for single-threaded pandas operations, but the memory controller on the LGA1700 platform really struggles when you populate all four slots with high-density sticks. Ive had so many crashes trying to get 128GB stable on an i9 rig, its honestly not worth the stress if this is ur professional work... Heres what I recommend: basically move into the Xeon W territory. I'm currently using the Intel Xeon W7-2495X Processor for my data science projects and the stability is just night and day. Since you mentioned MKL, these workstation chips handle those math libraries like a total dream without the insane thermal throttling issues you get on the i9. Plus, you get the extra PCIe lanes which is huge if you ever wanna scale up ur GPU setup for local LLM training later on. If you do decide to stay with the i9 for the clock speed, you highkey gotta get a massive cooler or you're gonna hit 100C in seconds during a heavy NumPy simulation. But seriously, for the 128GB+ RAM requirement and the overnight reliability, the Xeon platform is the way to go. No complaints here, it just works!!

• Intel Xeon W7-2495X Processor
• Intel Core i9-14900K Desktop Processor
• ASUS Pro WS W790-ACE Motherboard Hope that helps, peace