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Memory prices climb 500% in 12 months, up to 10x the lowest ever tracked prices — 128GB of DDR5 now $3,399
(www.tomshardware.com)
this post was submitted on 19 Aug 2026
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Machine learning/data science stuff.
Local LLMs is half of it.
But that aside, I found myself working with huge files that swapped like crazy on my 32GB of RAM, or were completely infeasible to work with. Janky local experiments I wouldn’t want to run on a cloud instance billed by the minute.
128GB has been a godsend, but I could completely fill 192GB and still swap some if I had it.
But I found there are happy side effects to having so much disk cache, too. For instance, game modding/tinkering has sped up immensely over 32GB. So has hashing for transfers, or other scenarios where it’s useful to already have a file cached in RAM.
Fair, I guess I'd also want some ram if I did not have access to a compute cluster.
But to be fair, for what I do I don't really think you can fit that much ram on a consumer motherboard.
In machine learning I don't generally find memory to be the bottleneck, as long as the GPU can handle it.
But sure, opening huge files can be big trouble.
I can rent cloud stuff, but it's just not worth it for casual/experimental use.
Like keeping Deepseek V4 loaded and hitting it rarely, but I want it to be in my control... it's just easier to do it locally.
Or experiments I launch like 30 times before it works. I'd waste hours moving all my stuff to a cloud instance, configuring it, tinkering with the experiment to get it to launch; the thing would be idle the vast majority of the time. And yeah, I know I can containerize stuff, but some projects I can't even test without a sizable memory pool.