How to Run gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC 5-Minute Setup

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How to Run gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC 5-Minute Setup

Running this model locally is fastest when deployed through a PowerShell script.

Please follow the instructions listed below to get started.

The process automatically pulls down gigabytes of critical model assets.

The installer diagnoses your environment to deploy the most compatible profile.

🔒 Hash checksum: 4ed72d1668afb91c9f119d3a660c7cfa • 📆 Last updated: 2026-06-29
  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • RAM: enough space for background apps and OS overhead
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • 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.

Parameters 26 B
Quantization 4‑bit QAT with MLX
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