I’ve just re-discovered ollama and it’s come on a long way and has reduced the very difficult task of locally hosting your own LLM (and getting it running on a GPU) to simply installing a deb! It also works for Windows and Mac, so can help everyone.
I’d like to see Lemmy become useful for specific technical sub branches instead of trying to find the best existing community which can be subjective making information difficult to find, so I created [email protected] for everyone to discuss, ask questions, and help each other out with ollama!
So, please, join, subscribe and feel free to post, ask questions, post tips / projects, and help out where you can!
Thanks!
What would you recommend to hook to my home assistant?
Perhaps give Ramalama a try?
https://github.com/containers/ramalama
Totally depends on your hardware, and what you tend to ask it. What are you running? What do you use it for? Do you prefer speed over accuracy?
I have a MacBook 2 pro (Apple silicon) and would kind of like to replace Google’s Gemini as my go-to LLM. I think I’d like to run something like Mistral, probably. Currently I do have Ollama and some version of Mistral running, but I almost never used it as it’s on my laptop, not my phone.
I’m not big on LLMs and if I can find an LLM that I run locally and helps me get off of using Google Search and Gimini, that could be awesome. Currently I use a combo of Firefox, Qwant, Google Search, and Gemini for my daily needs. I’m not big into the direction Firefox is headed, I’ve heard there are arguments against Qwant, and using Gemini feels like the wrong answer for my beliefs and opinions.
I’m looking for something better without too much time being sunk into something I may only sort of like. Tall order, I know, but I figured I’d give you as much info as I can.
Honestly perplexity, the online service, is pretty good.
As for local running, one question first: how much RAM does your Mac have? This is basically the factor for what model you can and should run.
8GB
8GB?
You might be able to run Qwen3 4B: https://huggingface.co/mlx-community/Qwen3-4B-4bit-DWQ/tree/main
But honestly you don’t have enough RAM to spare, and even a small model might bog things down. I’d run Open Web UI or LM Studio with a free LLM API, like Gemini Flash, or pay a few bucks for something off openrouter. Or maybe Cerebras API.
…Unfortunely, LLMs are very RAM intensive, and >4GB (more realistically like 2GB) is not going to be a good experience :(
Good to know. I’d hate to buy a new machine strictly for running an LLM. Could be an excuse to pickup something like a Framework 16, but realistically, I don’t see myself doing that. I think you might be right about using something like Open Web UI or LM Studio.
Actually, to go ahead and answer, the “fastest” path would be LM Studio (which supports MLX quants natively and is not time intensive to install), and a DWQ quantization (which is a newer, higher quality variant of MLX models).
Hopefully one of these models, depending on how much RAM you have:
https://huggingface.co/mlx-community/Qwen3-14B-4bit-DWQ-053125
https://huggingface.co/mlx-community/Magistral-Small-2506-4bit-DWQ
https://huggingface.co/mlx-community/Qwen3-30B-A3B-4bit-DWQ-0508
https://huggingface.co/mlx-community/GLM-4-32B-0414-4bit-DWQ
With a bit more time invested, you could try to set up Open Web UI as an alterantive interface (which has its own built in web search like Gemini): https://openwebui.com/
And then use LM Studio (or some other MLX backend, or even free online API models) as the ‘engine’
Alternatively, especially if you have a small RAM pool, Gemma 12B QAT Q4_0 is quite good, and you can run it with LM Studio or anything else that supports a GGUF. Not sure about 12B-ish thinking models off the top of my head, I’d have to look around.
This is all new to me, so I’ll have to do a bit of homework on this. Thanks for the detailed and linked reply!
I was a bit mistaken, these are the models you should consider:
https://huggingface.co/mlx-community/Qwen3-4B-4bit-DWQ
https://huggingface.co/AnteriorAI/gemma-3-4b-it-qat-q4_0-gguf
https://huggingface.co/unsloth/Jan-nano-GGUF (specifically the UD-Q4 or UD-Q5 file)
they are state-of-the-art at this size, as far as I know.
Awesome, I’ll give these a spin and see how it goes. Much appreciated!
I’m going to go out on a limb and say they probably just want a comparable solution to Ollama.
OK.
Then LM Studio. With Qwen3 30B IQ4_XS, low temperature MinP sampling.
That’s what I’m trying to say though, there is no one click solution, that’s kind of a lie. LLMs work a bajillion times better with just a little personal configuration. They are not magic boxes, they are specialized tools.
Random example: on a Mac? Grab an MLX distillation, it’ll be way faster and better.
Nvidia gaming PC? TabbyAPI with an exl3. Small GPU laptop? ik_llama.cpp APU? Lemonade. Raspberry Pi? That’s important to know!
What do you ask it to do? Set timers? Look at pictures? Cooking recipes? Search the web? Look at documents? Do you need stuff faster or accurate?
This is one reason why ollama is so suboptimal, with the other being just bad defaults (Q4_0 quants, 2048 context, no imatrix or anything outside GGUF, bad sampling last I checked, chat template errors, bugs with certain models, I can go on). A lot of people just try “ollama run” I guess, then assume local LLMs are bad when it doesn’t work right.