gemma-4-26B-A4B-it-QAT-MLX-4bit Using Pinokio Full Method
Using Docker is the absolute quickest way to install this model on your local machine.
Simply follow the directions outlined below.
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The setup auto-streams the model assets (expect a multi-GB download).
The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.
gemma-4-26B-A4B-it-QAT-MLX-4bit is a large language model built on the Gemma architecture with 26 billion parameters and optimized for instruction following. It leverages A4B design principles to improve inference efficiency while maintaining high fidelity in generation tasks. Through quantized aware training (QAT) and MLX optimizations, the model achieves compact 4‑bit representation without significant loss in accuracy. The resulting model excels in multilingual understanding, reasoning, and code generation, making it suitable for both research and production environments. Its reduced memory footprint enables deployment on consumer hardware and edge devices, broadening accessibility for developers. A quick reference of its core specs is provided below.
| Parameters | 26 B |
| Quantization | 4‑bit QAT with MLX |
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