How to Run gemma-4-26B-A4B-it-QAT-MLX-4bit No-Internet Version

Deploying locally takes the least amount of time when executed through native OS tools.

Follow the guidelines below to continue.

Everything happens automatically, including the heavy cloud asset download.

To guarantee smooth performance, the process auto-selects the best options.

🔒 Hash checksum: 128e3d9f49090ba8da5b4b30d96bc8bc • 📆 Last updated: 2026-07-03

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CPU: AVX2/AVX-512 instruction set required for llama.cpp
RAM: 64 GB to avoid OOM crashes on large contexts
Disk Space: free: 80 GB on system drive for scratch space
GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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