Selfhosted

Ollama with gemma 4 for LLM stuff, coding brainstorming, etc.

Comfy ui with z-image or stable diffusion for images.

I have the setup, never found a use for it though.

Yep.

Ollama + about 8 different models at the moment, hosted on a mac mini with open webui as a front end.

Predominantly for transcription, translation, an extra round of security checks on code, a more context friendly home assistant interface, and a daily run of context evaluation on property I'm looking for with a lot of specific needs (acreage, min elevation change, soil type, area, etc).

I don’t host it exactly, just use it when I don’t use my graphics card for gaming. I run Qwen3.6-35b on my 16gb vram RX 9700 xt with 34t/s. I use it as an IT advisor, admin and Linux teacher for my cachyOS gaming PC.

Hell naw my homelab is already sucking way too much power and running too hot.

I'm using anythingllm. It's quite easy to setup and use. I'm impressed of the perf on comodity hardware.

I started out playing around with code generation using Ollama/open-webui and qwen 2.5 coder 14b on a 3060 12GB, but ended up on a winding journey with an ex datacenter card called the AMD V620. Its roughly equivalent to an RX 6800XT, but with double the VRAM. At this point i've really done nothing productive with it but learned a lot about bios settings, GPU/ROCm drivers, and custom fan solutions/PWM controls trying to get it setup and optimized haha.

It's pretty sick though, that amount of VRAM with 512GB/s bandwidth can run Qwen 3.6 27B dense with 100k context window at 20 tokens/sec in LM studio. Draws 300 watts at the wall on my ITX chassis (idling about 30w).

I've been dabbling in building an aviation weather and field condition report application using this, but my next step is to rebuild my VS Code environment into a new machine. I'm kinda enjoying just fucking around with building the hardware too though

No, I'm not interested in that topic

Jup. Ollama and OpenWebUI is a great stack to tinker with some LLM models. They're kinda useful for aggregating large datasets, translations, frontend development and gathering relevant sources for me to read into. Also, Qwen has been amazing in understanding frameworks without documentation and writing one for me. I had to use some self-developed PHP framework for a task once and without qwen, I would've taken probably two more weeks to get the task done.

MiniCPM has also been REALLY good at image detection, describing it as accurately as possible, feeding it into qwen who then searches what the object could be and returning the result. I always liked google lense and that stack gave me a TEMU-Version of google lense that isn't quite as reliable, but definitely very useful.

No I don't. Unforunetly using Claude (asking myself everyday why tf cuz I don't do crazy shit) but trying to move on to LumoAI even meaby will buy a premium version to check this out formyself.

I started running LLMs a couple months ago on my own hardware. I have a Framework Desktop that I ordered last year and also recently picked up a refurbished 24GB AMD RX 7900 XTX which I'm doing some performance testing against. The dGPU is much better for dense models, and slightly faster for MoE if I'm willing to run them at a lower quant -- but uses more power and has annoying coil whine. The Framework Desktop uses ~100W under load, is quieter, and for the MoE models already runs them fast enough for most of my needs -- so most of my LLM use happens on that system still.

For software: I'm using ollama on the Framework currently, but I want to replace it with just using llama.cpp directly eventually. I've been using llama-cli for testing the dGPU. I wrote my own chat client to interact with ollama as well as a few other programs for specific tasks.

I've been using the LLMs for a mix of research (both personal and professional), entertainment, practical coding tasks (mostly debugging and brainstorming, plus a bit of UI prototyping, automatic generation of sequence diagrams for documentation, and light scripting), as well as automation of tedious tasks.

As an example of the latter, people often send me requests to prepare data sets by email but don't specify the sources they want precisely so I have to go match the name against the real name in our archives; LLMs are great for mapping the imperfect name -- with typos, missing prefixes, incorrect addition of spaces, addition/removal of hyphens, etc. -- to the exact name I actually need to pull the data off disk when given a lookup table to compare against.

As far as models go, I'm mostly using various Qwen 3.6 and Gemma4 variants. I have multiple versions of each for different purposes. llmfan46's uncensored Qwen 3.6 35B-A3B @ Q6_K (from Hugging Face) is my default model currently.

Well, I don't exactly host AI. But some of my software uses AI and/or machine learning. My photo gallery does face detection, I've installed text to speech and speech to text. My Home Assistant has a voice satellite (which is a poor-man's Alexa because I lack the hardware to do voice recognition in realtime). And I also regularly try some large language models and chatbots. But I don't have any real application (yet). And it's slow without a proper GPU. So I'm more or less just messing around. Currently that's with Ministral 3.

Yes. Currently using Gemma4:12b behind OpenWebUI and Hermes Agent plus a few lighter models for OCR and tagging in Paperless.

i don't use it at all, i do want some selfhosted speech to text model (whisper?) but my computer is ancient so it would be awfully slow. i have some multi hour audio recordings from presentations, would be nice to have them in text and searchable..

Yep.

I have a RTX 3090 + 128GB CPU RAM.

Currently I run my own custom IQ3_KT quantization of MiMo 2.5 300B, and it’s crazy good. It’s better than API models from not that long ago, and it’s served at about reading speed.

Never thought I’d ever run such a thing on my lowly desktop.

For quick scripts or code assistant, sometimes I use Qwen 27B (another custom quant, currently experimenting with exllama). Or Gemini 12B for messing with image/audio input. But TBH MiMo 2.5 with thinking disabled is smarter than 27B with it.

…And honestly, I use GLM 5.2 API a good bit.

I was lucky enough to get a yearly subscription for like $30, 6 months ago. I do self host the UIs or whatever takes the prompts, though.

I have a simple slow model running on CPU in my cluster for karakeep. I've tried running a variety of models on my 7900XT but even with 16GB their performance just isn't there. My new work m5 Mac book with 48GB of ram is the first time I've seen usable performance for local models and it has been pretty impressive.

I run Handy with Parakeet for speech to text, and home assistant with Whiper for the same. Whisper+ on my phone.

I think that counts. But I have more relevant and useful things to do on my hardware and no 2000€+ to get LLM-capable hardware 😂