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Comment by nico | Hacker News Reader

How does this compare to using a multimodal model like gemma3 via ollama?

Any benefit on a Mac with apple silicon? Any experiences someone could share?

Two things:

1. Because the support in llama.cpp is horizontal integrated within ggml ecosystem, we can optimize it to run even faster than ollama.

For example, pixtral/mistral small 3.1 model has some 2D-RoPE trick that use less memory than ollama's implementation. Same for flash attention (which will be added very soon), it will allow vision encoder to run faster while using less memory.

2. llama.cpp simply support more models than ollama. For example, ollama does not support either pixtral or smolvlm

By the way - fantastic work again on llama.cpp vision support - keep it up!!

Thanks Daniel! Kudos for your great work on quantization, I use the Mistral Small IQ2_M from unsloth during development and it works very well!!

:)) I did have to update the chat template for Mistral - I did see your PR in llama.cpp for it - confusingly the tokenizer_config.json file doesn't have a chat_template, and it's rather in chat_template.jinja - I had to move the chat template into tokenizer_config.json, but I guess now with your fix its fine :)

Ohhh nice to know! I was pretty sure that someone already tried to fix the chat template haha, but because we also allow users to freely create their quants via the GGUF-my-repo space, I have to fix the quants produces from that source

Glad it all works now!

Won’t the changes eventually be added to ollama? I thought it was based on llama.cpp

As far as I understand (not affiliated, just a user who peeked at the code), Ollama started out using llama.cpp as a runner for everything. But eventually they wrote their own runner in Golang, which is where they add support for new models. So most models you run via Ollama uses llama.cpp, but new stuff their own Golang runner.

On the other hand ollama supports iSWA for Gemma 3 while llama.cpp doesn't. iSWA reduces kv cache size to 1/6.

What’s iSWA? Can’t find any reference online

interleaved sliding window attention

Gemma 3 has some layers with a context size of 1024 tokens and others having full length. You need to read the Gemma technical report.

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