r/LocalAIServers

Need advice for a $10,000 AI workstation build (video, image, voice, LLMs, training, everything)

I’m planning to go very deep into the AI space and I want to build a serious workstation with around a $10,000 budget.

Main use cases:

- Local LLMs
- AI image generation
- AI video generation
- Voice cloning / speech models
- Fine-tuning and training
- Running multiple AI tools simultaneously
- Heavy VRAM workloads
- Stable Diffusion / Flux / ComfyUI
- Open-source models
- Maybe some game dev / rendering too

I want something that will still be powerful and relevant for the next few years instead of becoming obsolete immediately.

What hardware configuration would you recommend today for this budget?

Questions I’m specifically confused about:

1. CPU:
   Should I go Intel or AMD for AI workloads?
   Is Intel actually better for compatibility/stability or is AMD better now?

2. GPU:
   I know NVIDIA is basically mandatory for CUDA, but which setup makes the most sense?

- Single RTX 5090?
- Dual 4090s?
- Multiple GPUs?
- Used enterprise GPUs?
- Wait for newer cards?

3. Motherboard:
   Does Intel CPU + NVIDIA GPU + Intel motherboard work “best together” in terms of compatibility/stability?

Or does motherboard brand/platform not really matter much as long as PCIe lanes, RAM support, and power delivery are good?

4. RAM:
   How much RAM is realistically needed now?
   128GB?
   256GB?

5. Storage:
   What’s the smartest storage setup for AI workloads?
   Separate NVMe drives for models/cache/projects?

6. Cooling + PSU:
   How crazy do cooling and PSU requirements get once you start doing heavy AI workloads 24/7?

7. Linux vs Windows:
   Do most serious AI people just use Linux at this point?
   Is Windows still okay for heavy AI work?

I’d really appreciate recommendations from people actually doing AI locally instead of generic gaming-PC advice.

If you were building the best possible AI workstation around $10k today, what exact parts would you choose and why?

Hi All, I'm wondering how many TBs of storage you would fill with locally saved LLMs if you thought they would become unavailable online for download. I'm thinking about both large and small models, like a snapshot of the best of everything there is available online right now. Could be for coding, for writing, or for automation/robotics. Assuming that you also have the hardware to run models of any size, what's in your bugout load out if the grid goes down?

Hey everyone! I'm checking out GPU market for a local LLM. I'm interested in the mi50 16GB and the v100 16GB (the 32GB versions of both GPUs are unjustifiably expensive).

Here’s what I’ve noticed while researching the topic:

V100 - the "safe" option that just works. But there's a catch: it's SXM2, so you need to buy a PCIe adapter + cooling. Ideally, you could mount cooling from a 5090-4090 (or something simpler), and then you can probably forget about overheating.

The only downside is that everything will cost more, but it'll work fine if you set it up right.

mi50 - in terms of specs, it's better than the v100, but I see some serious (in my view) problems:

- Different BIOS versions that need to be installed depending on task. Like using the Radeon VII BIOS to make it work in consumer motherboards, but sellers usually sell them already flashed, so that shouldn't be an issue.

- "Insufficient multithreading" - https://www.reddit.com/r/LocalAIServers/comments/1koltfb/comment/mt1ihpe/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button - the commenter is likely talking about vLLM.

- Old ROCm - requires some tricks with .env (which isn't a problem), but if you need anything beyond LLM inference (for example, if you want to fine-tune a model), then big problems start to arise. With the v100, these issues are much less frequent (CUDA, after all).

On the plus side, the mi50 is cheaper than a bare v100 SXM2 (and the mi50 comes with a heatsink and PCIe by default).

Also, a downside for both is the lack of flash-attention-2 support, which means newer models might just not work (though it's unclear if they won't work in vLLM or llama.cpp).

So the question remains: knowing these nuances, which is the better choice? Keeping in mind that I'll likely buy several GPUs.

I stopped using cloud AI tools entirely for the past week.

Everything now runs locally.

What surprised me wasn’t performance — it was how my workflow started changing in unexpected ways.

Feels like we’re closer to personal AI stacks becoming normal than I thought.

Has anyone else fully committed to local setups

Done on-premises enterprise AI coding deployments at three different organizations. The gap between vendor documentation and operational reality is consistent enough to write up.

What vendors undersell is that the initial model selection and sizing is more consequential than they imply. The model that produces acceptable inference latency for 50 developers on your hardware may produce unacceptable latency for 200. Getting sizing right before committing to hardware is genuinely difficult and vendor estimates are optimistic. Context engine configuration is also more work than "connect it to your repos" on complex enterprise codebases.

What IT teams overestimate is the ongoing operational overhead. Once the deployment is stable it's much lower than most internal teams expect. It's infrastructure maintenance. The tools designed for enterprise AI coding deployments have admin interfaces that don't require deep AI expertise to operate. The things that go wrong are things IT teams already know how to handle.

The organizations that struggle with on-premises AI coding are the ones that either chose hardware before understanding real sizing requirements or tried to do it without someone who's done a deployment before owning the initial configuration.

Hey guys,

I am building a - in my opinion - pretty advanced Agent OS right now but I am not out for ads and I hope you can help me out:

Tell me the most important things that come to your mind, if you think about agentic AI Systems - specifically about Agent OS Systems. Which capabilities should a system you would actually use have?

Are you guys even interested in local-first GDPR compliant architectures?

You would really help me by bringing your thoughts to me.

Thanks in advance!