Zero-Click Run gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC with Native FP4 2026/2027 Tutorial

For the fastest local setup of this model, Docker is the best choice.

Please follow the instructions listed below to get started.

1-click setup: the app automatically fetches the large weight files.

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

📘 Build Hash: 0094fe20e3ab318bd24a295ca39e71eb • 🗓 2026-06-26 Verify Processor: next-gen chip for heavy context processing RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: free: 80 GB on system drive for scratch space Graphics: 12 GB VRAM minimum required for basic quantization

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.

Parameters	26 B
Quantization	4‑bit QAT with MLX

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