Google Just Fixed the Biggest Problem in Multimodal AI (Gemma 4 12B)

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00:00:00Google just unveiled their newest Gemma 4 12 billion parameter model and this is a game changer.
00:00:06No, seriously, this is not clickbait. This model is in fact a game changer in the way it is built.
00:00:13The thing that separates this from all other AI models is the fact that it is entirely
00:00:18encoder free. Now, what does that mean and how does it work and why is this such a big deal?
00:00:24Well, those are all good questions that we will explore in today's video. It's going to be a lot
00:00:29of fun. So let's dive into it. So the Gemma 4 12 billion model has a new architecture that
00:00:39completely breaks away from how every other multimodal model works. Multimodal model. Oh my
00:00:46God, that's such a such a tongue twister. So to understand why this is such a big deal, we have
00:00:51to look at how every other multimodal model handles things right now. Language models are built to read
00:00:57tokens, basically chunks of text turned into numbers. They don't naturally know what a pixel is or what a
00:01:05sound wave looks like. So usually we tape different models together. If you give AI an image, a massive
00:01:11vision encoder intercepts it first. It spends tons of processing power translating those raw pixels into a
00:01:19language the LLM can actually understand. And same goes for audio. A separate speech encoder has to
00:01:25translate the sound waves first. By the time the actual brain of the AI gets the data, you're running three
00:01:32separate networks at the same time. On a standard laptop, this completely hogs your VRAM and slows
00:01:38everything down. But Google DeepMind looked at this issue and thought, what if we can just cut out the
00:01:44middleman? So in the Gemma 412 billion model, they completely deleted the heavy vision encoder. Instead,
00:01:51when you feed it an image, the model chops it into small 48 by 48 pixel patches. And instead of passing
00:01:58those patches through dozens of layers of a separate vision network, the raw pixels pass through a single
00:02:04thin mathematical step called linear projection. And this linear projection is just a massive grid of numbers
00:02:11that takes 2304 pixel values, because that correlates to a 48 by 48 pixel square, multiplies them in a
00:02:19single step, and stretches them out into a single row that perfectly matches the LLM's text token
00:02:26format. So it doesn't just analyze what's in the image yet, it just reformats the raw data so it can fit
00:02:32through the model. And if you look at standard models, their vision encoders are massive. Like for example,
00:02:38this one has 550 million parameters. That's because a traditional encoder needs a lot of data to reshape,
00:02:45map, and understand the image. It has dozens of internal attention layers calculating relationships
00:02:50between pixels, trying to figure out where the edges are, what are the shapes, and what the objects might be
00:02:57before it even hands it to the text model. But DeepMind shrunk it by completely deleting all of that heavy
00:03:04brain power. They realized that the main language backbone is already incredibly smart and has plenty
00:03:10of layers to do the actual visual reasoning. So by removing all those thinking layers, they were left with
00:03:17just 35 million parameters, and that is literally just the raw physical count of connection weights needed
00:03:24to map those pixel grids into a text format. So it's a static single layer map that works for every image.
00:03:30Because it does zero internal thinking, it takes up practically no processing power, freeing up the VRAM
00:03:37and letting the main LLM handle the actual intelligence natively. And to understand how that single step works,
00:03:44you have to look at what's actually happening inside a language model backbone. Every language model has an
00:03:50internal formatting rule called a hidden dimension. Think of it like a standardized tray size. Whether it's
00:03:56the word apple or a piece of code or a punctuation, everything that gets fed into the LLM must be converted
00:04:04into this specific massive list of numbers because it has to match the dimensions of the matrices. And this raw
00:04:1148 by 48 pixel patch is just a grid of 2304 individual color numbers. If you try to feed that raw chunk
00:04:19directly into the LLM, the model will reject it because the dimensions don't actually match. And that is
00:04:26exactly why that 35 million parameter mapping layer exists. It is literally a single massive grid of
00:04:33connection weights that multiplies those 2304 pixel values and stretches them out into a single row that
00:04:40perfectly matches the LLM's text token format. It does zero analytical thinking, it just acts as a format
00:04:48converter so the data can slide right into the main transformer where the actual visual reasoning happens
00:04:54natively. And the model does something similar to audio reasoning as well, but for audio it's even simpler.
00:05:01So the way they managed to get rid of the audio encoder is by taking a raw 16 kilohertz audio signal and
00:05:07slicing it into continuous 40 millisecond frames. Each little frame contains exactly 640 floating point
00:05:15numbers describing the sound wave. The model takes those 640 floats and runs them through a similar
00:05:21simple projection layer that maps them straight into the language model's input space. To the transformer
00:05:28backbone, a 40 millisecond audio block looks identical to a continuous stream of text tokens. Because sound
00:05:35is already a chronological sequence, just like a sentence in a sequence of words, the LLM treats audio
00:05:42exactly like text. So this deep native integration lets the 12 billion parameter model handle live transcription,
00:05:49translation and text formatting in one single forward pass without forcing you to load separate speech
00:05:56networks into your memory. So this clever tactic is a massive win for running models locally on your own
00:06:02hardware. By stripping away all the encoder bloat, DeepMind managed to pack incredible reasoning
00:06:08power into a tiny footprint. And looking at the benchmark, it gets close to the performance of their massive 26
00:06:15billion parameter models, but it easily fits on a standard laptop with 16 gigabytes of VRAM
00:06:21or more. Plus Google included native multi-token prediction drafters right out of the box, meaning it predicts
00:06:28multiple tokens at a time for fast local inference speeds without forcing you to compress the model.
00:06:34So all of that sounds impressive. So now let's test it out and see how it works on my local M2 MacBook Pro.
00:06:41And some of the people in my previous OMLX video were asking how much VRAM do I actually have on my
00:06:48machine? So to answer that question, I have 24 gigabytes of VRAM. So that's what we're working with
00:06:53today. I also have to say this edge gallery application is so buggy. Like for example, if I try to add an
00:07:01image and ask, please analyze this image, it will instantly fail and give me this random error. And this
00:07:13is on the latest version. So unfortunately we couldn't test the vision encoder using the official AI edge
00:07:20gallery application, but there is another way we can actually test it out. Okay. So since I couldn't
00:07:26reliably test out the image processing with the Gemma for 12 billion model on Google AI edge gallery
00:07:34application, I decided to test it out on OMLX. And I also did a video about OMLX. It's an incredible
00:07:42framework for running AI models locally, specifically on Apple Silicon. And as you can see here, I have
00:07:47downloaded the eight bit quantized version of this model. So now I'm going to go to the chat section
00:07:54and let's see how fast it can actually do image reasoning in real time. So here I have a test folder
00:08:01with two images. One of them is just a screenshot of airport departures. So we'll use this image
00:08:09and ask what do you see in this image. And I want you to pay attention that I'm not speeding up this video.
00:08:18This is all real time. I want you to pay attention how fast it is able to do reasoning
00:08:24on such an image. So it is starting here, it's loading up the model, generating and boom, look at that.
00:08:33Look how fast it is able to parse through this picture and extract valuable information from it.
00:08:41First time I saw this on OMLX, I was genuinely blown away by the speed of it. It is absolutely insane.
00:08:50So I do have to say this is the best model that I tested out locally for image reasoning. And I also
00:08:57want you to pay attention to the fact that I'm running this model offline. I don't have my Wi Fi turned on.
00:09:03So now let's try another example. This one is just a blurry image of the TV show Vikings showing some
00:09:10characters. So once again, let's open up this image and ask the same thing. What do you see in this
00:09:21image? It's generating.
00:09:27And boom, look at that.
00:09:30I mean, that is just insane. This is so quick. I was so surprised.
00:09:37So yeah, I am honestly very, very impressed with the image processing performance of this new model.
00:09:43So there you have it, folks. That is the new encoder free Gemma 4 12 billion model in a nutshell.
00:09:50I was quite frustrated that I couldn't confidently test it out in their official AI edge gallery
00:09:56application. But as we saw, there are other alternative and maybe even better ways to run it
00:10:01locally. So I do think that this is a very nice model and it completely changes the future of running
00:10:07local AI models. Google DeepMind just basically proved that a single language backbone is smart enough
00:10:13to handle vision and sound natively. So this new technique will probably open doors to develop even
00:10:19more efficient multimodal reasoning models that can easily run on edge devices. So what do you think
00:10:26about the new Gemma model? Have you tried it? Will you use it? Let us know in the comments section down
00:10:32below. And folks, if you like these types of technical breakdowns, please let me know by smashing that like
00:10:37button underneath the video. And also don't forget to subscribe to our channel. This has been Andres
00:10:43from BetterStack and I will see you in the next videos.

Key Takeaway

Google's Gemma 4 12B model achieves high-performance multimodal reasoning on consumer hardware by using an encoder-free architecture that natively maps raw data directly into the language model's input space.

Highlights

  • Google's Gemma 4 12B model eliminates the need for heavy, separate vision or speech encoders, allowing for more efficient local AI processing.

  • The model utilizes a thin, 35-million-parameter linear projection layer to map raw pixel or audio data directly into the language model's input format.

  • By bypassing specialized encoder networks, the model frees up significant VRAM and allows the main LLM backbone to perform multimodal reasoning natively.

  • Gemma 4 12B features native multi-token prediction to enable faster inference speeds without requiring model compression.

  • The 12B parameter model achieves performance levels comparable to 26-billion-parameter models while remaining functional on hardware with 16GB of VRAM or more.

Timeline

Encoder-Free Architecture

  • Traditional multimodal models rely on bulky, external vision and speech encoders to process raw input data.
  • Gemma 4 12B replaces these large encoders with a simple linear projection step.
  • The new architecture converts raw input patches directly into a format compatible with the language model's internal hidden dimensions.

Conventional AI models use separate networks to translate raw pixels or sound waves into text-compatible tokens, consuming vast amounts of processing power and VRAM. Google's approach removes these intermediate networks. Instead of using a 550-million-parameter vision encoder, the new model uses a 35-million-parameter linear projection to map raw image data into the language model's token format.

Native Multimodal Reasoning

  • The model treats raw image data as a grid of pixel values and raw audio as continuous streams of floating-point numbers.
  • The language backbone performs reasoning natively because the transformed data matches the model's required input dimensions.
  • Native integration allows the model to handle tasks like transcription and translation in a single forward pass without additional speech networks.

By mapping inputs directly to the LLM's expected dimensions, the model enables the language backbone to handle visual and audio information directly. For audio, the system slices 16kHz signals into 40ms frames, treating the sequence of sound data identical to a sequence of text tokens. This eliminates the overhead of loading multiple separate networks into system memory.

Performance and Local Deployment

  • The 12B model maintains reasoning performance near that of 26-billion-parameter models.
  • Native multi-token prediction provides accelerated inference speeds on local devices.
  • Real-world tests on Apple Silicon demonstrate rapid image reasoning capabilities even while running offline.

Because the model strips away heavy encoder bloat, it can operate on standard laptops with at least 16GB of VRAM. Testing on an M2 MacBook Pro using the OMLX framework shows the model parsing complex images in real-time without active internet connectivity. This efficiency proves that a single, optimized language backbone is sufficient for complex multimodal tasks on edge devices.

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