Selfhosted

An aside for anyone reading this:

https://sleepingrobots.com/dreams/stop-using-ollama/

And that barely scratches the surface. Please.

Use anything but Ollama. Even APIs.

Yes. Openwebui/ollama for LLM, comfyui for stable diffusion. I just dick around with it as a toy.

Nope.

Running qwen3.6 27b through llama.cpp.

It's about as capable as sonnet 3.5.

I use it for light scripting, but real coding is done by cloud models.

I'm also using it as the brain for my Hermes agent. It sends me digests of news, subreddits, chats that I'd like to read but don't have time for. It does a great job researching things on the web for me, too.

I do, but I am becoming increasingly more disappointed as time goes on. Not just self hosted, llms in general. They sometimes help, but they mislead so many times and waste time that you don't even notice. I think that's the trap. When you succeed at a task, you become impressed but don't notice how many times it failed doing a simple task. And as soon as you scratch the surface, you see how you would have done it differently and perhaps in a better way. Even just googling is bad. It does research for you, but it has no critical thinking and can't decide what is better from the results it gets (other than google ranking) so it often leads you to think it did as good as you would, when it's nowhere near as good. Every time I did the googling myself after it did, I did it much better. And I mean MUCH better. Ask it to find the app, it misses the most important ones, hallucinates a bunch, for ex. I found this to be the case with frontier models as well.

Self hosting has its benefits, but seeing how the ecosystem looks right now, concluding this is a huge bubble is inevitable. It reminds me of crypto so much. It looks rich and plentiful, but as soon as you dig a mm under the surface - nobody has tested it, it's got a critical bug, it is overblown and there are issues with no response. No docs, no info, no nothing. For the biggest thing in technology in history, it is awfully hollow. I don't mean it in a condescending way, in fact community is enthusiastic and very helpful, it's just that it doesn't live up to what most would expect.

A caveat I need to mention is I have not used it for coding - I have an irrational fear and resistance towards it, being a programmer. I just won't touch it, even if it means the end of my career. I'm trying to be grown-up about it, but so far, I dont want to use it, for good and bad reasons.

Yes, I got a Strix Halo machine before the RAM price hike and use it to run all my ML stuff on it.

Currently using llama-swap with llama.cpp/ComfyUI and opencode/Open WebUI as frontend.

I'm running Qwen3.6-27b, Voxtral Mini 4b, Piper and Qwen Image. Also, some embedding and reranking models.

I use them for:

• Tagging and classification of my documents in Paperless
• Home Assistant (voice assistant)
• Translations (both text and image)
• Transcriptions
• Some light coding and debugging
• Avatar/Backdrop generation for DnD sessions

No. I still have no use for it and everything I use is automated without at a far lower footprint.

I've tried a few times but with only 8gig of vram it's simply not worth it.

I do, I use ollama. I mostly just tinker, but I use with with home assistant for a quasi Alexa like experience with the voice assistant, I use it for summarizing some YouTube transcripts in too lazy to read/watch, and I've tried to see how capable it is with coding.

I recently gave it a try with qwen3.5 and deepseek coder v2. I have a RTX3090 and these are the largest models that can run comfortably on it.

Conclusion, they are both fucking useless. Free tier claude runs circles.

If I wanted AI for some reason, it'd be self-host or nothing.

I've thought about it, but I actually could never think of anything I would do with it.

I tried but I only have 16g of ram and it wouldn't complete a thought alas

Technically, TTS/STT are mostly MLs; I'm pretty sure many people run these. I have a setup but I'm better with buttons that with spoken words, and I listen to ambient sounds or music. I think some day I'll make voice assistant for talking to while driving, but that's not a trivial task hardware-wise, even if I used cloud LLM layer, which I won't. Putting AI on baremetal sounds like an interesting project.

I have a homemade "local agent" that can actually "code" somewhat, I use it just to figure out how this thing works on the inside practically. Mostly useless otherwise (also I have GPU that's older than AI, so it's kind of fun technical task to run this stuff on pure RAM+swap). Feels like the whole hype is greatly overrated, but I appreciate a chance to learn something new anyway.

Running decencored Qwen3.6-27b and a 9b Gemma for RAG and scrapes on Ollama with a mostly vibe coded discord bot. Just got it to run tools and scrape and post news on a schedule. The first model I can run locally that's smart enough to be useful. May give Jan a try for the back end after reading that other guys rant.

Mostly use it for stupid questions I could have googled and to brag to friends.

I set up ollama on our thinkstation in the lab and I use it for looking up documentation, generating readmes, searching papers, and sometimes coding when I know what to do but don't feel it is worth it to spend time on it myself. So basically the chat with web search.

Why would I?

The other day I made a machine learning model that classifies images as either 'a certain type of undesirable image' (no, not porn) or 'any other image'. It is 96.4% accurate and takes 14 ms to classify one image (using CPU only - with a GPU it could be 5x - 10x faster).

I plan to offer this as an API service that social media networks can use to filter posts.

I ran through lmstudio because it really eazy, I ran some kind of qwen 3.6 27b imatrix neo code DI, it is the best local model for coding I tried, I think it can be better than some cloud model

Yup, ollama, various models. I initially downloaded it because I, along with thousands of other people, wanted to see what would happen if I made models debate with each other after RAGging them with various books (The Prince, The Art of War, The complete works of Shakespeare, etc.).

The results were uninteresting and I abandoned the project pretty quickly. I'll sometimes use them for code analysis but they're too slow on my rig to be really useful.

Yes, llama-swap and I use it for home assistant text-gen notifications, basic coding tasks, etc

If anyone here self-hosts definitely check out llama-swap as it has some nifty features for hotswapping LLMs, image generation models and voice models.

I hosted Qwen 3.5 9b uncensored on my site at https://masland.tech/ for a while. I didn't really use it and no one else used it so I took it down. These days I'm spending most of my time finding uses for AI and accessibility. One of the next things I'm planning is a video to text reasoning system, primarily for the purpose of grading used electronic devices.

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

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

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.

Partially. I started with hosting my own llama3.2 + granite4 models using Ollama for my Home Assistant smart home and for general chat with OpenWebUI. I also run whisper for speech-to-text locally on my 1080 Ti GPU. I like the privacy and ownership of my self-hosted models, but I started to run into limitations with the small weights. So I built some tools that allow me to selectively route traffic to larger models hosted on DeepInfra depending on my need. For example, to GLM/Kimi models for code reviews or for my custom harnesses or harder problems.

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 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

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

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.

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