Run gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 11

The fastest method for installing this model locally is by using Docker.

Follow the step-by-step instructions below.

The system automatically triggers a cloud download for all heavy weights.

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

???? Hash-sum: b0ba1d40137513b24e49f8348810d30a | ???? Last update: 2026-06-24 Verify Processor: 6-core 3.5 GHz minimum required RAM: minimum 16 GB for stable 8B model loading Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: stable 30+ tk/s at 4-bit quantization on medium setup

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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