r/SelfHostedAI

Hey guys, I’m currently building a setup for self-hosted AI (image generation, maybe some LLM inference later), and I’m trying to figure out whether PCIe 5.0 actually makes a difference compared to PCIe 4.0.

I already have a GPU that supports PCIe 5.0, so now I’m wondering if it’s actually worth pairing it with a PCIe 5.0 motherboard, or if PCIe 4.0 is still more than enough.

From what I understand, most GPUs today don’t fully saturate PCIe 4.0 yet, but I’m not sure if that changes for AI workloads—especially things like:

Stable Diffusion / image generation at scale

Running multiple models

High batch sizes or heavy data transfer between CPU and GPU

So my questions:

- Does PCIe bandwidth actually impact performance in real-world self-hosted AI use?

- In what scenarios would PCIe 5.0 give noticeable benefits over PCIe 4.0?

- Is it worth going full PCIe 5.0, or am I fine sticking with PCIe 4.0 even with a PCIe 5.0 GPU?

Dashboard

Spent a day building something called DiffusionDesk.

My goal wasn’t to make “another Stable Diffusion UI.”
It was to build a cleaner local-first frontend workstation that feels less like a pile of Python scripts duct-taped together and more like an actual desktop product.

Current focus:

• Local image generation (using ComfyUI in the backend)
• Model management
• Cleaner workflow UX
• Asset organization
• History / prompt tracking
• Apple Silicon support
• Simpler setup experience over time

https://preview.redd.it/3zly6s1y7d1h1.png?width=1552&format=png&auto=webp&s=c94c5aedd2cd0c8b4fdc28a65fba388a2d390a60

I love AUTOMATIC1111 and ComfyUI for what they are, but I always felt there was room for something that sits between:

• the raw power of ComfyUI
• and the ease of use of in-painting (ComfyUI was always a challenge for me to get it right)

Still early. Still rough in places. But it’s moving fast.

Would genuinely appreciate feedback from people deep in the local AI / SD ecosystem:

• What do you hate most about current ComfyUI/SD tooling?
• What would make you switch UIs?
• What features are still missing across the ecosystem?

Check it out on GitHub:
DiffusionDesk GitHub

Hey founders 👋

I’m Anant, founder of SparrowHost.

We’re offering $100 in startup credits for early-stage startups to deploy VMs on our infrastructure and reduce initial cloud costs.

Apply here:

https://sparrowhost.net/startup-credits

Would love to support fellow founders, hear your feedback, and connect with more builders

SOLAI just launched the $399 Solode Neo, a Linux-based “AI computer” built around an Intel N150 with 12GB RAM and a focus on always-on automation, browser tasks, and AI agents like Claude Code and Gemini CLI. The concept is actually pretty interesting - basically a dedicated low-power box that quietly runs workflows and automation 24/7 from your home network. That said, the marketing feels a little bigger than the hardware itself. This seems much more like a polished Linux automation appliance than some powerful local AI workstation, especially considering the modest specs. Still, I could absolutely see homelab folks and Linux nerds finding this appealing if the software stack is good and the setup really is painless.

Just want some general discussion started on fully offline / air gapped systems

Not trying to make any statements or take sides / start fights. Genuinely curious and want to see what you guys think:

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Say tomorrow something catastrophic happens and we don't have internet. Power is still up and running for basic functions but for whatever reason the internet is down (environment/politics/etc.). Doomsday scenario I know, but just hear me out

Could we somehow create our own offline version of Claude/chatgpt using local models only? Not as powerful of course, but with say $2000 could you build a semi decent working version?

\---
I say all this because I think maybe the question I'm trying to ask is could we all somehow feasibly separate AI from the cloud providers in a long term effort to safely get out of this whole monopolization mess?

Sorry if this isn't the right place for this discussion, I can post somewhere else if needed. Just want to get some ideas going

I might be totally oblivious to something so I'm sorry in advance if I'm asking stupid question lol

I deployed a rocket chat server, deployed CLI agents on my Mac using tmux. separated them by channel, used SSH config and SSH keys to connect to users on a VPS and scaled out an infrastructure to 250 clients to easily update and build their websites via prompts. I built a control agent to create sub domains and manage DNS and user setup.

Then I developed an App Store of apps I could bolt onto those agents, like email, sms, trading.

I had clients join that had no clue how to build a website, in those cases I updated the context file I injected on boot to say "this client has no idea how to build a website, ensure they use best standards and subtly correct them and educate them "

I sat back and watched the agent hilariously be the proxy I used to be and teach clients how to build good stuff.

The stack I used was simple and also embedded in context on agent boot.

I am now essentially managing context for 500 agents now(model specific) and helping clients unlock the potential of AI.

We stopped selling our product as a service and started saying hey learn how to use an agent by first building a website.

I've found my footing again after 3 years wondering in the remains of what was to understanding the question of what next.

agent control dashboards like these will be necessary for so many industries way beyond websites.

thoughts?

Hey I tried openclaw to run locally.

I chose ollama route sinse i am just a student and paying for the api and running openclaw on cloud would require money.

I tried ollama deepseek 1.5 B model which is small and fast and can be run on my laptop.

I have rtx 4050 with 6 gb vram.

Running the model just with ollama is fast and can run at speeds which I can easily work with but when I used openclaw and used that model there the query openclaw takes is very slow. Also other things in openclaw felt very slow.

Slow things include the reply which I get even hello is replied very late( not a problem when I single run model). The ui, when I go to different tabs like skills , channels,instances, sessions,cron jobs and other tabs they feel like taking 1 minute to load.

I want a solution to run openclaw faster locally( can't pay for web version as I just want to experiment)

I heard there was a lighter version for it but didn't understood what it means so recommended it ( idk what it is)

My model( even though small can run 9B or 7B model ) can be used in ollama. I can link lmstudio api so model running is not hard but openclaw itself is slow.

Everyone teaching openclaw is either in good hardware( mac or high end windows or Linux) or using web hosting so pc won't break I just want to experiment small and will do carefully.

Can anyone help with running it locally far better.

It is for people that live in the terminal and want an assistant that can help with Linux / admin / dev tasks while keeping control local.

Seeking first users and honest feedback, particularly on:

1. Does setup run clean on your machine ?

2. What provider/model works best for you?

3. Is the TUI fast and easy to understand?

4. Are tool permissions too tight, too loose, or just right?

5. What was HELM not good at?

If there's one thing you can test, run `helm init`, run the TUI with `helm`, do one real Linux task and tell me where it breaks.

I have an old MI Android phone with:

• 2GB RAM
• 32GB storage
• decent battery health
• always-on WiFi possible
• android 10
• octa core cpu 2.00 GHz
• Model M2006C3LI

I'm a software engineer and recently started experimenting with turning it into a mini homelab node.

Things I already tried:

• PicoClaw
• Termux-based setups
• lightweight SSH/server experiments

The issue is that most “old phone server” tutorials online feel gimmicky or impractical long-term.

I'm looking for genuinely useful or technically interesting ideas that fit low-resource hardware well.