How to Launch gemma-4-26B-A4B-it-QAT-MLX-4bit Full Method

Deploying this model locally is quickest when done via Docker.

Follow the step-by-step instructions below.

Then, execute the docker-compose up command to launch the model.

🗂 Hash: 87c8bfd14a1556e174f13a7fc4cf31cb • Last Updated: 2026-06-21 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: required: 16 GB absolute minimum for small models 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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