Epyc 7551? 256gb ddr4, r9700 + mi50. Running two models depending on what I’m doing . Llama.cpp + comfyUI or two llama.cpp for model and advisor in OMP. Main model is Qwen 3.8 27b abliterated

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

• 96gb ddr5
• intel i7 12th gen
• RTX 3060

it also doubles as my media ripper. I initially built it specifically for that before AI was even a thing.

I run too many models to count really. everything through CLI because I’m dissatisfied with all the sloppy UIs.

Ollama, openwebui, running the ~20GB Qwen models on a 3090

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 run Gemma4 12B on my RX9070 XT. it’s not permanently on, I just turn it on when I want to use it. Access it through a browser. Does what I want out of it. Nothing “agentic”.

I recently gave Strata a shot for my local AI. I’m very impressed with the performance they have eeked out of my hardware. I’ve got a rather beefy gaming desktop I built as an AI server back when the current craze started, before prices started going up, but models up until this point really failed to take full advantage of that hardware. Strata (which uses a Qwen model) both simplified the setup process, and it maxes out the hardware when running.

Strata acts like an OpenAI compatible API server, I then have Hermes as a “harness” to make an “agent”, which I interact with on “buzz”, which is like discord, but made for bots. Although Hermes also supports discord and several other chat platforms.

If you have the hardware to run Strata, I recommend setting it up, it comes with a basic chat interface, which you can then grill to assist you with setting up Hermes and whatever else.

One thing I didn’t mention, is security. I highly recommend you treat AI as unpredictable, unreliable, and capable of being taken over by bad actors. This isn’t a problem for a pure chat interface, but Hermes gives the AI tools to do things, and those tools could be used to take over your computer, scan your network, and create all kinds of havoc. For my part, Hermes is running in a virtual machine which is on an isolated network. I keep tight control on what it does and doesn’t have access to in my network main network. That may not be something you have the hardware or knowledge to do, at the very least I recommend running Hermes in a docker container, and not giving it access to your personal desktop nor credentials to things like your email account.

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 haven’t seen it already, take a look at [email protected]

I just got qwn3-4b-instruct working on an Intel A750 with 8 gigs of vram.

I use it to power Home Assostant/Voice Assistant on a few M5Stack echos around the house to replace cloud based smart speakers.

Qwen 3.8 flash next on a 4090, using strata instead of llama.cpp, gives me about 45-55 tokens/sec. Mainly used for coding tasks. The model size is perfect. If the qwen 4.0 models can be a bit less wordy in reasoning but the same size and quality they’ll be really strong.

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.

24GB GPU

Qwen 3.8 ( 2 variants )

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

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

Qwen 3.8 was a game changer for me.

As a service? No, not really.

I use llms for specific tasks, usually small models, for things like identification and tagging (immich, paperless), translation or transcription (whisper), etc. I have a bunch of larger models on a Mac mini and my work laptop for various testing purposes, and have used them to make summaries and other such things.

They aren’t great for search IMO, and search summaries can be quite bad.

I do find them useful to interrogate an unknown rs232 protocol or similar, though, which is where larger models come in handy.

Corporate LLMs are generally garbage to me not only because they are just data farming, but also because they really aren’t good at everything. They might claim it, but the reality is a tailored model is often the better approach.

Using openwebui as a front end for llama.cpp is a decent strategy, combined with sub-agent routing (so generic model hands off to tailored models then returns the right info). This can be useful for some scenarios.

A caveat on coding - IMO, they are mostly bad at it. If you are using it to generate a small part of something that needs to be repeated they are handy, but at scale they aren’t great, meaning for the entire project (or even an entire plugin or module, depending on complexity). Maintainability becomes a serious issue. While they have improved somewhat, there are limits to what they can do.

Macs with M series chips are good options for a hardware base since the memory is shared, and IMO is one of the most cost effective options over a dgpu with a ton of memory. A dgpu with a decent bit of memory (say 8-12GB) is a solid choice for a model thats larger (27b with 3 or 4b active as a mixture-of-experts model) especially if you have an iGPU for regular graphics tasks. Since its all shared on the M series macs, just get something that fits entirely in memory there.

Played around with it on my desktop machine. Someone’s distilled variant of Qwen 3.8-27B-Q4_K_M, on a intel12700k + RTX3090 24GB is what I use.

I haven’t adjusted the reasoning down from very high but I mostly use it for trying tweaks to half-finished coding projects I’ve lost motivation to complete many years ago. I used it to develop a very simple JS-free personal website. I have secure shell which I can access from Termux or PuTTY, but sometimes I have the llama.cpp webui insecurely accessible online for one day at a time just for fun.