Launch gemma-4-26B-A4B-it-QAT-MLX-4bit on Copilot+ PC

The fastest tactical way to launch this model locally is via a Docker image.

Make sure to follow the instructions below.

The script takes care of fetching the multi-gigabyte model weights.

To save you time, the system will automatically determine efficient resource allocation.

🖹 HASH-SUM: 0df1e1291f17a4fdc7136aa4f6e899fb | 📅 Updated on: 2026-06-25 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk: high-speed SSD 120 GB to cache model layers 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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