The fastest way to get this model running locally is via Docker.

Use the instructions provided below to complete the setup.

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

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🔍 Hash-sum: 7e3d4f9708ee98f63aca141e252fb6ee | 🕓 Last update: 2026-06-25 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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