Do you host your own ML / AI / LLM? What do you use, and what do you use it for?
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No, I have taste.
If I wanted AI for some reason, it’d be self-host or nothing.
I prefer my critical faculties completely intact and un-altered, thank you very much.
I do not require or desire a 400 watt bullshit-artist yes-man or vulnerability coder cooking my GPU.deleted by creator
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.
Do you mean Sonnet 4.5?
I don’t have the rig to run it at real speeds but I’ve played with it over API. Seems pretty good.
No. I still have no use for it and everything I use is automated without at a far lower footprint.
I currently run Qwen3.6-27b on llama.cpp and use it via openwebui. Mostly, I use it for web research via tavily, to a lesser extent for coding and interactively learning about things that are new to me but common in training data (such as basic math or ML concepts).
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.
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.
Acronyms, initialisms, abbreviations, contractions, and other phrases which expand to something larger, that I’ve seen in this thread:
Fewer Letters More Letters Git Popular version control system, primarily for code LTS Long Term Support software version SSH Secure Shell for remote terminal access
3 acronyms in this thread; the most compressed thread commented on today has 3 acronyms.
[Thread #27 for this comm, first seen 25th Jun 2026, 15:40] [FAQ] [Full list] [Contact] [Source code]
I’m using anythingllm. It’s quite easy to setup and use. I’m impressed of the perf on comodity hardware.
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
What sort of tok/s are you getting on the strix?
About 200 t/s prompt processing and 10-20 t/s with MTP.
Greatly depends on the task, predictable things like code generates at 18-20 t/s. Creative writing more like 10-17 t/s.
Damn - I thought strix would do a bit better than that, for how much it costs.
Given the 27b is a dense model, I think the numbers are quite ok. Curious about the quant tho.
The cool thing about the strix is its large unified memory, but it lacks memory bandwith for compute intensive workloads. Something like Qwen3.5-122b MoE with only like 12b active parameters might run at twice the speed if it fits the configuration.
Yeah. Though I think theres a new strix out soon (Medusa? Gorgon? Something like that).
Its a bit like my P40. On paper, it has 24GB. But that 24gb is capped at 400GB/s and the ai compute is what…Pascal era?
AI = Good, fast, cheap - pick 2
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.
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 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…
How ancient is ancient? TTS and STT are much lighter than llm. (eg: Whisper, Piper, Kokoro, Coqui etc)…you might have more capability than you think, especially if you’re doing batch processing like that.
a haswell xeon e5-1650 machine, i remember running llama 7b in llama.cpp in like 2023 and it was quite sluggish. guess i should try whisper at some point…
Ha. You were doing inference on CPU on a haswell era. Been there, done that.
OTOH…whisper.cpp is heavily optimised for it.
Plus, you’re doing batch transcription, not real-time, so slow doesn’t actually matter.
Fire Whisper small or medium overnight and wake up to searchable text.
PS: if you want a good fast little llm, something like Qwen 3.6 2B will work well on the Xeon.
