Ollama's new engine for multimodal models

The timing on this is a little surprising given llama.cpp just finally got a (hopefully) stable vision feature merged into main: https://simonwillison.net/2025/May/10/llama-cpp-vision/

Presumably Ollama had been working on this for quite a while already - it sounds like they've broken their initial dependency on llama.cpp. Being in charge of their own destiny makes a lot of sense.

Do you know what exactly the difference is with either of these projects adding multimodal support? Both have supported LLaVA for a long time. Did that require special casing that is no longer required?

I'd hoped to see this mentioned in TFA, but it kind of acts like multimodal is totally new to Ollama, which it isn't.

It's a turducken of crap from everyone but ngxson and Hugging Face and llama.cpp in this situation.

llama.cpp did have multimodal, I've been maintaining an integration for many moons now. (Feb 2024? Original LLaVa through Gemma 3)

However, this was not for mere mortals. It was not documented and had gotten unwieldy, to say the least.

ngxson (HF employee) did a ton of work to get gemma3 support in, and had to do it in a separate binary. They dove in and landed a refactored backbone that is presumably more maintainable and on track to be in what I think of as the real Ollama, llama.cpp's server binary.

As you well note, Ollama is Ollamaing - I joked, once, that the median llama.cpp contribution from Ollama is a driveby GitHub comment asking when a feature will land in llama-server, so it can be copy-pasted into Ollama.

It's really sort of depressing to me because I'm just one dude, it really wasn't that hard to support it (it's one of a gajillion things I have to do, I'd estimate 2 SWE-weeks at 10 YOE, 1.5 SWE-days for every model release), and it's hard to get attention for detailed work in this space with how much everyone exaggerates and rushes to PR.

EDIT: Coming back after reading the blog post, and I'm 10x as frustrated. "Support thinking / reasoning; Tool calling with streaming responses" --- this is table stakes stuff that was possible eons ago.

I don't see any sign of them doing anything specific in any of the code they link, the whole thing reads like someone carefully worked with an LLM to present a maximalist technical-sounding version of the llama.cpp stuff and frame it as if they worked with these companies and built their own thing. (note the very careful wording on this, e.g. in the footer the companies are thanked for releasing the models)

I think it's great that they have a nice UX that helps people run llama.cpp locally without compiling, but it's hard for me to think of a project I've been more by turned off by in my 37 years on this rock.

For one Ollama supports interleaved sliding window attention for Gemma 3 while llama.cpp doesn't.[0] iSWA reduces kv cache size to 1/6.

Ollama is written in golang so of course they can not meaningfully contribute that back to llama.cpp.

[0] https://github.com/ggml-org/llama.cpp/issues/12637

It's impossible to meaningfully contribute to the C library you call from Go because you're calling it from Go? :)

We can see the weakness of this argument given it is unlikely any front-end is written in C, and then noting it is unlikely ~0 people contribute to llama.cpp.

They can of course meaningfully contribute new C++ code to llama.cpp, which they then could later use downstream in Go.

What they cannot meaningfully do is write Go code that solves their problems and upstream those changes to llama.cpp.

The former requires they are comfortable writing C++, something perhaps not all Go devs are.

I'd love to be able to take this into account, step back, and say "Ah yes - there is non-zero probability they are materially incapable of contributing back to their dependency" - in practice, if you're comfortable writing SWA in Go, you're going to be comfortable writing it in C++, and they are writing C++ already.

(it's also worth looking at the code linked for the model-specific impls, this isn't exactly 1000s of lines of complicated code. To wit, while they're working with Georgi...why not offer to help land it in llama.cpp?)

Perhaps for SWA.

For the multimodal stuff it's a lot clear cut. Ollama used the image processing libraries from Go, while in llama.cpp they ended up rolling their own image processing routines.

Citation?

My groundbreaking implementation passes it RGB bytes, passes em through the image projector, and put the tokens in the prompt.

And I cannot imagine sure why the inference engine would be more concerned with it than that.

Is my implementation a groundbreaking achievement worth rendering llama.cpp a footnote, because I use Dart image-processing libraries?