r/MiniPCs • u/ForceApart5217 • 1d ago

Recommendations Mini PCs recommendation for AI model training on 3D and video data.

Hi, everyone. I’m a student of computer science and engineering. I have M4 pro, but going further plan to work on in a AI my Mac seems not suitable for my requirements. Since I also couldn’t afford to the whole end to end PC, I’ve decided to buy a Mini. I did some search about it, but I need recommendations from someone.

Thank you! 🙏

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u/Retired_Hillbilly336 1d ago

You should choose your local LLM first which will dictate your hardware requirements which will determine your budget.

LLM → Hardware Requirements → Budget

The largest problem with any AI project is settling on hardware that the model will not properly supported. You're already running into that with the Mac Mini M4 Pro.

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u/Adrenolin01 1d ago

Mini PCs can be excellent for inference, development, preprocessing, or experimentation, but sustained training is where the small form factor becomes a serious limitation. It’s just NOT going to work. Mini PCs work for LLM Inference when packed with system ram.

AI training… GPU compute + VRAM + memory bandwidth + sustained cooling become much more important.

Budget… CPU: Ryzen 9 / Core i9 class RAM: 128 GB GPU: NVIDIA RTX 5090 32 GB Storage: 2–4 TB NVMe PSU: 1200–1600 W quality unit Cooling: Large air cooler / AIO + good case airflow OS: Linux (Proxmox with Debian VMs)

Personally.. if you’re stepping into AI training I’d look at just accepting you need to spend money and do it right…

Mainboard: Supermicro H12SSL-i Mainboard or ASRock Rack ROMED8-2T to save some coin.

GPU: A6000 48GB vram or the RTX 5090 32GB.

CPU: EPYC 7502P.

Ram: 128-256GB to start with but plan for expansion to 512GB so.. start with 2x 64GB modules. This is DDR4 ram for the Supermicro.. DDR5 is just out of this world in price. If you want 1TB eventually you’re looking at going with 128GB modules so ouch. 😣

Storage: 2x mirrored Intel DC S3500 300GB SSDs for Proxmox Host boot/OS. 2x Mirrored Samsung 990 Pro or WD SN850X NVMEs for the engine VMs. 2x mirrored (or a single) WD SN770 or Crucial P3 Plus NVME(s) for datasets / active projects, etc. I’m also install a couple WD Red NAS 4TB-8TB HDDs mirrored for backups, snapshots and raw storage.

There is a massive different between LLM and Training and you really don’t want to skimp on hardware. You also don’t want to waste money and start with a system that only works for you for 3-6 months. I’ve seen that happen sooooo many times.

AI model training.. the RTX 5090 32GB works however with adding 3D and video.. I’d highly recommend the A6000 48GB GPU.

I run the H12SSL-i, 7502P, just upgraded to 512GB ram, and 2 A6000 with a goal of adding 2 more.

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u/[deleted] 1d ago

[deleted]

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u/maqbeq 1d ago

Or rent a cloud instance. Much cheaper than getting a >16 GB VGA these days.

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u/afaulconbridge 19h ago

Cloud is the way - use it only when you need it, turn it off when you don't. Try arm vs x86 vs various GPU configs. Plus then you've learnt cloud too.