Setup gemma-4-26B-A4B-it-QAT-MLX-4bit For Low VRAM (6GB/8GB)
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Setup gemma-4-26B-A4B-it-QAT-MLX-4bit For Low VRAM (6GB/8GB)

The most efficient approach for a local installation is leveraging Docker containers.

Check out the detailed setup guide below to begin.

Everything happens automatically, including the heavy cloud asset download.

The script runs a quick hardware check to dynamically adjust parameters for elite speed.

🖹 HASH-SUM: e712466b0a5d90fc5cb9ed8861705796 | 📅 Updated on: 2026-07-03
  • Processor: high single-core performance needed for token latency
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

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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