How to Autostart gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC No Python Required Offline Setup

Using Docker is the absolute quickest way to install this model on your local machine.

Follow the step-by-step instructions below.

The installer automatically pulls the model (could be multiple GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

🧾 Hash-sum — 35cf99872d0e574bd74a48a301c88aed • 🗓 Updated on: 2026-06-27 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers GPU: modern architecture (Ada Lovelace / Ampere minimum)

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.

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