Quite a niche release. The MoE outperforms it on score and will likely be faster thanks to lower active weights. So this really only makes sense for specific ram constrained applications that can’t fit a quantized MoE
Gemma 4 12B: A unified, encoder-free multimodal model
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Re: Gemma 4 12B: A unified, encoder-free multimodal model
#52I do enjoy the immediate out of touch signaling with the "runs on your 16gb vram laptop" line. Because everyone has a laptop with 16gb vram, or can just pop out and buy a new one, right?
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Re: Gemma 4 12B: A unified, encoder-free multimodal model
#53The big story here is the encoder-free part, which I still don't fully understand. > Vision: We replaced Gemma 4’s vision encoder with a lightweight embedding module consisting of a single matrix multiplication, positional embedding and normalizations. That's technically encoding, just without using a dedicated model for it like SigLIP? The Developer's Guide elaborates, it's still a 35M layer which I am curious is ro…
Embedded within that developer page is a good explainer of the encoder free architecture . https://newsletter.maartengrootendorst.com/p/a-visual-guide-...
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#54Quite aside from the architectural changes, I suppose this is the answer to why Google had such a glaring hole in the (pretrained) Gemma4 model lineup between the Gemma4 4b and Gemma4 26b models!
A model that comfortably fits in 16GB of VRAM (allowing room for context) is a welcome upgrade.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#55What are the use cases for these small models? Is there anyone using models of this scale in their daily life who could share their experience?
I've yet to see someone answer a question like this with a decent, useful answer.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#56I don’t see the download in lm studio
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#57What's Google's business case for releasing open models? Don't get me wrong, I am grateful and appreciative of these releases. I'm trying to understand how it fits into their bigger picture as a for profit company? Are they not helping competitors build on the novel technology they have developed? Is it simply goodwill and/or marketing? Or am I missing something strategic?
Evangelism for AI. Google is one of the big AI providers.
Eventually the local model is not enough, and you'll upgrade to the big ones.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#58What are the use cases for these small models? Is there anyone using models of this scale in their daily life who could share their experience?
"Small" models are the ones I can run myself on my own terms. LLMs aren't useful enough for me to justify spending hundreds of euros on a GPU with 16GB VRAM or something, and that's assuming I have the rest of the desktop just laying around. Back when I checked (before the RAM price hike), these models weren't meaningfully better than 4-8GB ones anyway, you'd have to go for the top tier cards at 24 or 32 GB iirc to get something vaguely in the direction of the SaaS versions, and that was absolutely out of my budget. Even if that changed, so have hardware prices so it'd probably still work out the same
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#59using an embedder instead of a decoder is quite clever. Not sure who came up with that first but it's a cool idea.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#60Am I missing something or are the Ollama versions of this (https://ollama.com/library/gemma4/tags) text-only for now?