Llama.cpp running qwen 3.8 on a mini PC with 32GB ddr5 ram and 8GB vram. Not the most speedy but it works. I only really access it while at home but I'm also set up with tailscale.

I self-host my own neural network, all within the confines of my own skull.

I wouldn't mind running a model at home, mostly to help document my setup. But I just don't trust it. So no.

I'm self-hosting immich (with ML-based facial recognition, semantic search and OCR) and psiTTSa, an offline text-to-speech engine.

(Prob not the answer you were looking for, but a reminder that "AI" is more than just LLMs.)

A quick glance shows that I'm in the minority. I grabbed a Bosgames M5 with 128GB UMA RAM when I discovered openclaw (at the time claudebot). I'm running Lemonade, which is a wrapper for llama.cpp, sd-cpp, flux, and others. I'm happy to have paid a "mere" $2400 compared to what similar systems cost now, and now I'm toying with fine tuning my own models. Though, I primarily run Hermes Agent paired with Qwen3.6 35b A3B (using a heretic ablitieration for 0 refusals).

Hermes Agent does a lot of random stuff for me right now. It manages my calendar and to-do list in nextcloud via MCP server. It created and maintains an llm-wiki based on my Obsidian Vault. Then it creates a report every morning about both and the weather.

The server has tailscale installed, so yes I can access it anywhere.

I just bought a 32GB M6 Mac Mini that I'll also use for local ai. Will probably use Qwen3.8-27B. Accessing it from outside my lan would happen via Tailscale.

• Qwen3.6 35b a3b Q4_K_M running on llama.cpp
• Open-WebUI as a frontend
• integrations with an embedding model and searxng & crawl4ai
• doing lots of different stuff with it, asking basic stupid questions, recipes, tech stuff, etc

Not only do I run and share it. I've built an entire service to allow everyone else to do so safely. https://aihorde.net/

Running Qwen 3.8 27B dense on some R9700s, and Qwen 3.8 flash next on dgx sparks. I use it for code gen outside work, including an automated Dev pipeline that rubs on its own. When I did the math comparing to token API pricing of Opus 4.8, it covered its own costs in a few months. With the sparks I get about 40tg/s, 1500pp/s, but can get concurrency up to 5 stably. With the R9700s, I get ~100tg/s, 3000pp/s and can also get up to 5 concurrency.

I access it over wireguard to the network that it is on, and use termux to ssh into a build machine for dev over CLI using https://pi.dev/

I also started building https://fcli.dev/ to make integrations easier; the idea being that LLMs should just implement simple reusable functions instead of repeatedly making complex curl requests to integrate with a service.

I also use LLMs heavily for work; Qwen 3.8 seems almost on par to Opus 4.8 IMO. I also don't have to worry about token costs, and it often responds faster than Claude would because there usually isn't a queue on my machines. At this point, I wouldn't complain if my work provided me 2x B6000 instead of a Claude subscription; I'd still be able to get all my work done.

What a timely comment.

I recently gave my kid a gaming PC for his birthday...which I used as an excuse to upgrade my GPU from a 1660 Super to a 5060ti 16GB

Combined with a 5600x3d and 32gb of system memory.

I just started messing with local LLMs yesterday. "Getting started" is rather easy. pacman -S lm-studio (I use arch, btw), launch it, download a model that your hardware supports, and start talking.

Right now I'm working on getting it to talk to vscode.

If you are playing around at home I suggest seeking a MoE model. You get less penalty from having less vram and offloading to normal ram. I run Qwen 3.6 quant 6 on 250k context on a 16gb vram 64gb ram ddr4 PC. I get about 31tps which is enough for my use. After a little tweaking I have put results of it's output into Claude Pro for vetting and it hasn't found any flaws in the simple scripts and things I've asked it to make. I can only run Qwen 3.8 which is a dense model and not MoE. At low quant with not as much context. It spits out accurate short context answers if that's all you need. So I prefer 3.6. Tiel is faster but Claude picked up more errors in what I was doing. Orinith kept getting into thinking loops for me so I stopped using it. Anyway I'm just a little guy.

I use Qwen 3.8 27b Q4 with 100k context (llama.cpp and the Mistral AI harness) on a 24 GB AMD Radeon 7900 XTX and it's been great. It can handle pretty much anything I throw at it and I can let it work indepedently overnight / over days on long tasks.

Yes, were all on c/LocalLLaMa

I'm running qwen3.8-next-flash on a rx7900xtx plus 128gb of ram. It serves my hermes agent. I direct it through matrix. I also forward an endpoint to my VPS to use for anything. Mostly opencode.

Yes! I’ve got quite a few projects going right now. Probably too many of them to keep track

I am the only user, so it’s relatively easy to run multiple models for what ever task. Ollama is pretty good at balancing which models are working at what time to not bog down they system

I’m running gpt-oss 120 for a general purpose Hermes agent. Out of the box, it was ok. Not very snappy, but it was one of the best ones for orchestrating other agents.

Glm flash (4.something?) does pretty good as well.

I’ve found a hybrid of cloud and local is the best solution. Use local for smaller or more defined tasks. Use cloud for orchestrating, planning, and…fixing the local agents mistakes lol

I’ve got an LLM maintained Obsidian wiki that is written and edited by a few Ollama agents based on chats via my own self hosted matrix instance. I’ve taken it to a point where I’m pretty happy with the results. But I think if I had enough time and energy, I could really dial it in.

One of my favorite use cases for this is when I’m researching topics like UFOs/high strangeness/conspiracy theories. I have my very own LLM maintained Obsidian vault filled with curated notes and links to sources.

Literally, no one cares about this and they think it’s stupid and I’m stupid and I should feel bad for spending time on this or having the audacity of sharing it… they just don’t appreciate the amount of work and knowledge it took to get what I have set up.

I think it’s cool as fuck. I geek out every time that it works.

Oh, forgot to mention I am running it on a Mac studio with 96 gig of unified memory. I think it’s an M3 ultra chip.

Even ignoring all of the functional issues with LLMs/"AI" related to their design to never admit to not knowing and preferring to make stuff up instead (hallucinate), I have an ethical qualms about the way they are trained. Two primary issues are that they aggressively scrape data from sources that explicitly do not consent to sharing their work and the data centers which have made hardware impossibly expensive for everyone but them and are so impactful to our underfunded electrical grids that they cause brown outs that keep people from heating their homes among other things, cause major spikes that cause house fires and destroy home appliances, and create an excuse to generate power using the worst polluting generators that we had finally gotten shut down to slow climate change (personally I'm tired of the wildfires and extreme storms from climate change).

So, I personally consider it extremely unethical to use the technology for that and several other reasons which are hard to touch on exhaustively in a post like this.

24GB GPU

Qwen 3.8 ( 2 variants )

Pangolin>OpenWebUI>lama.cpp for general use and available outside the homelab

Zoo Code>lama.cpp for coding , mostly scripts and html

Qwen 3.8 was a game changer for me.

I self host both at home and at work.

At work (for completion and generation) we have a now quite old A6000 running mainly Qwen 3.6 (though I'm trialling 3.8 from Monday) with OpenClaw as the harness. The use case is mainly triage and pre-work on bugs. We have an agent which can be given an exception report with a bunch of information like stack trace and local variable values and it will go off and gather the required code, order files, and data to analyse the issue and come up with a fair idea of the cause and possible solution. This saves devs and support team time. Also looking at adding a PA agent to this which will be able to read emails and calendars and be able to set up (with approval) meetings and move things around and generally make someone's life a bit less admin-y without having to hire a full PA role.

Also technically in the AI space we have a fleet of Pis with Hailo AI hats running object and anomaly detection in visible and LWIR spectrums for QC

I wish I could. I don't have the hardware for it.

Not me. Call me old-fashioned, but I prefer organic.