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Google Gemma

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Gemma is a family of open-weight language models released by Google for text generation and related NLP tasks.

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Also known as gemma 2·gemma 3·gemma 4·gemma 3n·gemma 4 mtp

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Key Takeaway Gemma 4's smallest model runs on 3GB of VRAM, and it's the one I actually reach for
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gemma 2 gemma 3 gemma 4 gemma 3n gemma 4 mtp
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Gemma 4's smallest model runs on 3GB of VRAM, and it's the one I actually reach for

Gemma is a family of open-weight language models released by Google for text generation and related NLP tasks.

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What is Gemma 4, anyway?

So, what exactly is Gemma 4? It is basically the lightweight open-weight alternative to the massive Gemini models. Google changed the architecture to make these models work on different types of hardware. For example, if you are a desktop user, you can use Gemma 4 31B, which specializes in deep reasoning and complex coding. It is ideal for high-end GPUs. Gemma 4 26B is another capable model if you have a low-end GPU. It activates only 4 billion parameters at a time, and it strikes the perfect balance between speed and intelligence. Edge models are where things get interesting for mobile users.

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How does MTP improve Gemma 4?

The process uses a technique called “Speculative Decoding,” in which the drafter models predict upcoming words in the prompt even before the main Gemma model has read through it. While the drafter moves on to the next sequence of words, the main model verifies the predicted set of words at the same time.

Google's latest trick gets Gemma 4 running 3x faster right on your phone
Which local AI models can do this?

The good news is that you don't need a massive, GPU-melting frontier model for this workflow. Smaller models are perfectly capable of identifying missing context and asking useful follow-up questions. Popular options include lightweight open models like Google's Gemma 4, Meta's Llama 4 Scout, Microsoft's Phi-4, and compact models from Mistral and Qwen. These models are readily available as mentioned through tools like LM Studio and GPT4All, running comfortably on standard consumer hardware.

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