It looks like !buildapc community isn’t super active so I apologize for posting here. Mods, let me know if I should post there instead.
I built my first PC when I was I think 10-11 years old. Built my next PC after that and then sort of moved toward pre-made HP/Dell/etc. My last PC’s mobo just gave out and I’m looking to replace the whole thing. I’ve read over the last few years that prefabs from HP/Dell/etc. have gone to shit and don’t really work like they used to. Since I’m looking to expand comfortably, I’ve been thinking of giving building my own again.
I remember when I was a young lad, that there were two big pain points when putting the rig together: motherboard alignment with the case (I shorted two mobos by having it touch the bare metal of the grounded case; not sure how that happened but it did) and CPU pin alignment so you don’t bend any pins when inserting into the socket.
Since it’s been several decades since my last build, what are some things I should be aware of? Things I should avoid?
For example, I only recently learned what M.2 SSD are. My desktop has (had) SATA 3.5" drives, only one of which is an SSD.
I’ll admit I am a bit overwhelmed by some of my choices. I’ve spent some time on pcpartpicker and feel very overwhelmed by some of the options. Most of my time is spent in code development (primarily containers and node). I am planning on installing Linux (Ubuntu, most likely) and I am hoping to tinker with some AI models, something I haven’t been able to do with my now broken desktop due to it’s age. For ML/AI, I know I’ll need some sort of GPU, knowing only that NVIDIA cards require closed-source drivers. While I fully support FOSS, I’m not a OSS purist and fully accept that using a closed source drivers for linux may not be avoidable. Happy to take recommendations on GPUs!
Since I also host a myriad of self hosted apps on my desktop, I know I’ll need to beef up my RAM (I usually go the max or at least plan for the max).
My main requirements:
Intel i7 processor (I’ve tried i5s and they can’t keep up with what I code; I know i9s are the latest hotness but don’t think the price is worth it; I’ve also tried AMD processors before and had terrible luck. I’m willing to try them again but I’d need a GOOD recommendation)
At least 3 SATA ports so that I can carry my drives over
At least one M.2 port (I cannibalized a laptop I recycled recently and grabbed the 1TB M.2 card)
On-board Ethernet/NIC (on-board wifi/bluetooth not required, but won’t complain if they have them)
Support at least 32 GB of RAM
GPU that can support some sort of ML/AI with DisplayPort (preferred)
Nice to haves:
MoBo with front USB 3 ports but will accept USB 2 (C vs A doesn’t matter)
On-board sound (I typically use headphones or bluetooth headset so I don’t need anything fancy. I mostly listen to music when I code and occasionally do video calls.)
It didn’t matter to me if it was in stock; just wanted a place to start. Advice is very much appreciated!
EDIT: WOW!! I am shocked and humbled by the great advice I’ve gotten here. And you’ve given me a boost in confidence in doing this myself. Thank you all and I’ll keep replying as I can.
Since the TPU is a matrix processor instead of a general purpose processor, it removes the memory access problem that slows down GPUs and CPUs and requires them to use more processing power.
Just no. Flat out no. Just so much wrong. How does the TPU process data? How does the data get there? It needs to be shuttled back and forth over the bus. Doing this for a 1080p image with of data several times a second is fine. An uncompressed 1080p image is about 8MB. Entirely manageable.
Edit: it’s not even 1080p, because the image would get resized to the input size. So again, 300x300x3 for the past model I could find.
/Edit
Look at this repo. You need to convert the models using the TFLite framework (Tensorflow Lite) which is designed for resource constrained edge devices. The max resolution for input size is 224x224x3. I would imagine it can’t handle anything larger.
Not a single model is larger than 40MB. Whereas LLMs start at well over a big for even smaller (and inaccurate) models. The good ones start at about 4GB and I frequently run models at about 20GB. The size in parameters really makes a huge difference.
You likely/technically could run an LLM on a Coral, but you’re going to wait on the order of double-digit minutes for a basic response, of not way longer.
OK mman, dont pop a vein over this. I’m a hobbyist, with some experience, but a hobbyist nonetheless. I’m speaking from personal experience, nothing else. You may well be right (and thanks for the links, they’re really good for me to learn even more).
I guess, at the end of the day, the OP will need to make an informed decision on what will work for him while adhering to his budget.
I’m glad to be here, because I can help people (at least some times) and learn at the same time.
I just hope the OP ends up with something that’ll fit his needs and budget. I will he adding a K80 to my rig soon, only because I can let go of 50 bucks and want to test it until it burns.
I wish you all a very nice weekend, and keep tweaking, its too Much fun.
That’s incredibly rude. At no point was I angry or enraged. What you’re trying to do is minimize my criticism of your last comment by intentionally making it seem like I was unreasonably angry.
I was going to continue with you in a friendly manner, but screw you. You’re an ass (and also entirely wrong).
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A lot of what you said is true.
Just no. Flat out no. Just so much wrong. How does the TPU process data? How does the data get there? It needs to be shuttled back and forth over the bus. Doing this for a 1080p image with of data several times a second is fine. An uncompressed 1080p image is about 8MB. Entirely manageable.
Edit: it’s not even 1080p, because the image would get resized to the input size. So again, 300x300x3 for the past model I could find.
/Edit
Look at this repo. You need to convert the models using the TFLite framework (Tensorflow Lite) which is designed for resource constrained edge devices. The max resolution for input size is 224x224x3. I would imagine it can’t handle anything larger.
https://github.com/jveitchmichaelis/edgetpu-yolo/tree/main/data
Now look at the official model zoo on the Google Coral website.
https://coral.ai/models/
Not a single model is larger than 40MB. Whereas LLMs start at well over a big for even smaller (and inaccurate) models. The good ones start at about 4GB and I frequently run models at about 20GB. The size in parameters really makes a huge difference.
You likely/technically could run an LLM on a Coral, but you’re going to wait on the order of double-digit minutes for a basic response, of not way longer.
It’s just not going to happen.
OK mman, dont pop a vein over this. I’m a hobbyist, with some experience, but a hobbyist nonetheless. I’m speaking from personal experience, nothing else. You may well be right (and thanks for the links, they’re really good for me to learn even more).
I guess, at the end of the day, the OP will need to make an informed decision on what will work for him while adhering to his budget.
I’m glad to be here, because I can help people (at least some times) and learn at the same time.
I just hope the OP ends up with something that’ll fit his needs and budget. I will he adding a K80 to my rig soon, only because I can let go of 50 bucks and want to test it until it burns.
I wish you all a very nice weekend, and keep tweaking, its too Much fun.
That’s incredibly rude. At no point was I angry or enraged. What you’re trying to do is minimize my criticism of your last comment by intentionally making it seem like I was unreasonably angry.
I was going to continue with you in a friendly manner, but screw you. You’re an ass (and also entirely wrong).
OK.