The responsiveness between a hard drive and an SSD is night and day. NVMe is even faster but not noticable unless you move a hell of a lot of data around. A motherboard having at least 1 M.2 NVMe slot is common, so installing the OS on it is an option. Hard drives have more storage per price, but unless space is significant factor I suggest using SSDs (also quieter than a spinning disk!). More info on storage formats in this video
Rescent generations of motherboards use DDR5 RAM, which were very expensive on release. I think the price has come down but I am not up to date this generation. You may be able to save money making a DDR4 system but you’ll be stuck on a less supported platform.
AMD had like ~10 years of bad/power hungry processors and Intel stagnated, rereleasing 4-core processors over and over. AMD made a big comeback with their Ryzen series becoming best bang for buck, then even over taking Intel. I think it’s pretty even now.
If you don’t intend to game or do certain compute workloads then you can avoid buying a GPU. Intergrated CPUs have come quite far (still low end compared to a dedicated GPU). Crypto mining, Covid and now AI has made the GPUs market expensive and boring. Nvidia has more higher-end cards, mid range is way more expensive for both and low end sucks ass. On Linux AMD GPUs drivers come with the OS, but Nvidia you have to get their proprietary drivers (Linux gaming has come a long way).
jjlinux@lemmy.ml 8 months ago
DDR5 has gone down dramatically compared to launch. You can get 64GB with a very fast bus for under 200 dollars now. At launch 32GB would easily set you back 250+. AMD has made a killing with Ryzen. Never mind the new naming convention that Intel came up with to make it even more complicated to choose the right CPU for your use cases, ridiculous. As for Nvidia GPU drivers, at the end of the day, they just work, regardless their proprietary drivers philosophy (which, again, I agree sucks). But if the OP is going to be doing AI development, machine learning and all that cool stuff, he’d be better served by getting a few CUDA TPUs. You can get those anywhere from 25 dollars to less than 100, and they come in all types (USB, PCI, M.2). coral.ai/products/#prototyping-products I have 1 USB Coral running the AI on my Frigate dicker for 16 cameras, and my CPU never reaches more than 12% while the TPU itself barely touches 20% utilization. You put 2 of those bad boys together, and the CPU would probably not even move from idle 🤣
CeeBee@lemmy.world 8 months ago
Those aren’t “CUDA” anything. CUDA is a parallel processing framework.
Also, those devices are only good for inferencing smaller models for things like object detection. They aren’t good for developing AI models (in the sense of training). And they can’t run LLMs. Maybe you can run a smaller model under 4B, but those aren’t exactly great for accuracy.
At best you could hope for is to run a very small instruct model trained on very specific data (like robotic actions) that doesn’t need accuracy in the sense of “knowledge accuracy”.
And completely forgot any kind of generative image stuff.
jjlinux@lemmy.ml 8 months ago
Same reply. And you can add as many TPUs as you want to push it to whatever level you want. At 59 bucks a piece, they’ll blow any 4070 out of the water for the same or less cost.
fubbernuckin@lemmy.world 8 months ago
Hold on a second, how come every time i look for TPUs i get a bunch of not-for-sale nvidia and Google cards, but this just exists out there and i never heard of it?