Google Gemma

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.

The Gemma 4 family of open-weights models from Google includes four variants, spanning a range of sizes from 2B effective parameters to 31B parameters and including both Mixture of Experts (MoE) and dense architectures.  These multimodal models ingest text, vision, and for select variants, audio inputs and generate text outputs. They support context sizes of up to 256K tokens, and have been trained for thinking, coding, function calling, optical character recognition (OCR), object recognition and automatic speech recognition tasks. For relatively compact models they have outstanding language s

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.

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.