> Ollama user with the opposite question -- why not? What am I missing out on? I'm using it as the backend for playing with other frontend stuff and it seems to work just fine.

Used to be an Ollama user. Everything that you cite as benefits for Ollama is what I was drawn to in the first place as well, then moved on to using llama.cpp directly. Apart from being extremely unethical, The issue is that they try to abstract away a bit too much, especially when LLM model quality is highly affected by a bunch of parameters. Hell you can't tell what quant you're downloading. Can you tell at a glance what size of model's downloaded? Can you tell if it's optimized for your arch? Or what Quant?

`ollama pull gemma4`

(Yes, I know you can add parameters etc. but the point stands because this is sold as noob-friendly. If you are going to be adding cli params to tweak this, then just do the same with llama.cpp?)

That became a big issue when Deep Seek R1 came out because everyone and their mother was making TikToks saying that you can run the full fat model without explaining that it was a distill, which Ollama had abstracted away. Running `ollama run deepseek-r1` means nothing when the quality ranges from useless to super good.

> And as someone running at 16gb card, I'm especially curious as to if I'm missing out on better performance?

I'd go so far as to say, I can *GUARANTEE* you're missing out on performance if you are using Ollama, no matter the size of your GPU VRAM. You can get significant improvement if you just run underlying llama.cpp.

Secondly, it's chock full of dark patterns (like the ones above) and anti-open source behavior. For some examples:

1. It mangles GGUF files so other apps can't use them, and you can't access them either without a bunch of work on your end (had to script a way to unmangle these long sha-hashed file names) 2. Ollama conveniently fails contribute improvements back to the original codebase (they don't have to technically thanks to MIT), but they didn't bother assisting llama.cpp in developing multimodal capabilities and features such as iSWA. 3. Any innovations to the do is just piggybacking off of llama.cpp that they try to pass off as their own without contributing back to upstream. When new models come out they post "WIP" publicly while twiddling their thumbs waiting for llama.cpp to do the actual work.

It operates in this weird "middle layer" where it is kind of user friendly but it’s not as user friendly as LM Studio.

After all this, I just couldn't continue using it. If the benefits it provides you are good, then by all means continue.

IMO just finding the most optimal parameters for a models and aliasing them in your cli would be a much better experience ngl, especially now that we have llama-server, a nice webui and hot reloading built into llama.cpp

April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini