There is virtually no reason to use Ollama over LM Studio or the myriad of other alternatives.
Ollama is slower and they started out as a shameless llama.cpp ripoff without giving credit and now they “ported” it to Go which means they’re just vibe code translating llama.cpp, bugs included.
I really like LM Studio when I can use it under Windows but for people like me with Intel Macs + AMD gpu ollama is the only option because it can leverage the gpu using MoltenVK aka Vulkan, unofficially. We're still testing it, hoping to get the Vulkan support in the main branch soon. It works perfectly for single GPUs but some edge cases when using multiple GPUs are unsupported until upstream support from MoltenVK comes through. But yeah, I agree, it wasn't cool to repackage Georgi's work like that.
Why is ollama so many people’s go-to? Genuinely curious, I’ve tried it but it feels overly stripped down / dumbed down vs nearly everything else I’ve used.
Lately I’ve been playing with Unsloth Studio and think that’s probably a much better “give it to a beginner” default.
Ollama is good enough to dabble with, and getting a model is as easy as ollama pull <model name> vs figuring it out by yourself on hugging face and trying to make sense on all the goofy letters and numbers between the forty different names of models, and not needing a hugging face account to download.
So you start there and eventually you want to get off the happy path, then you need to learn more about the server and it's all so much more complicated than just using ollama. You just want to try models, not learn the intricacies of hosting LLMs.
Ollama got some first-mover advantage at the time when actually building and git pulling llama.cpp was a bit of a moat. The devs' docker past probably made them overestimate how much they could lay claim to mindshare. However, no one really could have known how quickly things would evolve... Now I mostly recommend LM-studio to people.
LM Studio has been around longer. I’ve used it since three years ago. I’d also agree it is generally a better beginner choice then and now.
Unsloth Studio is more featureful (well integrated tool calling, web search, and code execution being headline features), and comes from the people consistently making some of the best GGUF quants of all popular models. It also is well documented, easy to setup, and also has good fine-tuning support.
By desk you mean that "Mac mini"? Because it is pricey. In my country it is 1000 USD (from Apple for basic M4 with 24GB). My desk was 1/5th of that price.
And considering that this Mac mini won't be doing anything else is there a reason why not just buy subscription from Claude, OpenAI, Google, etc.?
Are those open models more performant compared to Sonnet 4.5/4.6? Or have at least bigger context?
In case someone would like to know what these are like on this hardware, I tested Gemma 4 32b (the ~20 GB model, the largest Gemma model Google published) and Gemma 4 gemma4:e4b (the ~10 GB model) on this exact setup (Mac Mini M4 with 24 GB of RAM using Ollama), I livestreamed it:
The ~10 GB model is super speedy, loading in a few seconds and giving responses almost instantly. If you just want to see its performance, it says hello around the 2 minute mark in the video (and fast!) and the ~20 GB model says hello around 5 minutes 45 seconds in the video. You can see the difference in their loading times and speed, which is a substantial difference. I also had each of them complete a difficult coding task, they both got it correct but the 20 GB model was much slower. It's a bit too slow to use on this setup day to day, plus it would take almost all the memory. The 10 GB model could fit comfortably on a Mac Mini 24 GB with plenty of RAM left for everything else, and it seems like you can use it for small-size useful coding tasks.
Ollama is slower and they started out as a shameless llama.cpp ripoff without giving credit and now they “ported” it to Go which means they’re just vibe code translating llama.cpp, bugs included.
And didn't Ollama independently ship a vision pipeline for some multimodal models months before llama.cpp supported it?
Hmm, the fact that Ollama is open-source, can run in Docker, etc.?
brew install llama.cpp
use the inbuilt CLI, Server or Chat interface. + Hook it up to any other app
Lately I’ve been playing with Unsloth Studio and think that’s probably a much better “give it to a beginner” default.
So you start there and eventually you want to get off the happy path, then you need to learn more about the server and it's all so much more complicated than just using ollama. You just want to try models, not learn the intricacies of hosting LLMs.
What does unsloth-studio bring on top?
Unsloth Studio is more featureful (well integrated tool calling, web search, and code execution being headline features), and comes from the people consistently making some of the best GGUF quants of all popular models. It also is well documented, easy to setup, and also has good fine-tuning support.
It's essentially a model that's learned to do the absolute minimum amount of work while still getting paid. I respect that enormously.
It scores 1441 on Arena Elo — roughly the same as Qwen 3.5 at 397B and Kimi k2.5 at 1100B.
Ollama v0.19 switched to Apple's MLX framework on Apple Silicon. 93% faster decode.
They've also improved caching so your coding agents don't have to re-read the entire prompt every time, about time I'd say.
The gist covers the full setup: install, auto-start on boot, keep the model warm in memory.
It runs on a 24GB Mac mini, which means the most expensive part of your local AI setup is still the desk you put it on.
And considering that this Mac mini won't be doing anything else is there a reason why not just buy subscription from Claude, OpenAI, Google, etc.?
Are those open models more performant compared to Sonnet 4.5/4.6? Or have at least bigger context?
https://www.youtube.com/live/G5OVcKO70ns
The ~10 GB model is super speedy, loading in a few seconds and giving responses almost instantly. If you just want to see its performance, it says hello around the 2 minute mark in the video (and fast!) and the ~20 GB model says hello around 5 minutes 45 seconds in the video. You can see the difference in their loading times and speed, which is a substantial difference. I also had each of them complete a difficult coding task, they both got it correct but the 20 GB model was much slower. It's a bit too slow to use on this setup day to day, plus it would take almost all the memory. The 10 GB model could fit comfortably on a Mac Mini 24 GB with plenty of RAM left for everything else, and it seems like you can use it for small-size useful coding tasks.