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 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.

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.

Yeah, I’m using qwen 31b a3b on an amd 9070xt requires a bit of cpu offloading, but still plenty fast. Using it wall llama.cpp. Combine that with some mcp’s such as ddg-search to make it truly useful by actually being able to search online.

I mostly use it for small tedious tasks with well defined inputs and outputs. For example when hyprland recently changed from their own configuration language to lua. At first I started going line by line translating my config to the new lua language until I realized oh wait this is exactly the type of thing that ML is useful for. Going from the well defined hyprland configuration language to their also well defined lua syntax. It banged it out in less than a minute with only a single mistake which I easily fixed. The mistake it made was that it forgot to translate the comments to lua. It did it in less than a minute and worked first try. Where as I had made several typos and gotten a few lines wrong when I was doing it by hand.

Not to say that I couldn’t do it. I would have gotten it done in about half an hour, but less than a minute is a lot faster.

I also used it to transform a bunch of unstructured data into json data, so that I could then use purpose built tools like jq to parse that. If I’m having trouble finding certain information. I’ll ask it to find me some resources to look at.

Basically small well defined tasks and parsing data is what I use it for and it seems to be pretty good at that.

What I don’t like is the way companies try to market it to people. I don’t believe people should be trying to summarize emails or messages from loved ones, writing essays or any other creative tasks for the most part. Translating is okay. I don’t expect a machine to be able to decide things for me or to be some filter between me and others.

Yes. My Actual Intelligence lives in my head, and runs mostly on coffee.

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 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.

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

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

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

Nope.

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.