Install gemma-4-26B-A4B-it-QAT-MLX-4bit Offline on PC For Low VRAM (6GB/8GB) Step-by-Step

Install gemma-4-26B-A4B-it-QAT-MLX-4bit Offline on PC For Low VRAM (6GB/8GB) Step-by-Step

The fastest method for installing this model locally is by using Docker.

Use the instructions provided below to complete the setup.

An automated background process downloads all required large-scale files.

Without any user input, the software calibrates parameters for optimal hardware usage.

📦 Hash-sum → 8852675bc531ba0e6d3749d6fac36928 | 📌 Updated on 2026-07-04
  • Processor: Intel i5 or AMD Ryzen 5 for basic 7B models
  • 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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