Run gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 11 No Admin Rights

Run gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 11 No Admin Rights

Using Docker is the absolute quickest way to install this model on your local machine.

Just follow the guidelines provided below.

The setup auto-streams the model assets (expect a multi-GB download).

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🖹 HASH-SUM: 4ef3e620ca6b6e82c12cd696edb4180e | 📅 Updated on: 2026-06-28
  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB highly recommended for 26B+ GGUF models
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • 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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  • Intro movie and sponsor splash screen skip patch for instant loading
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