gemma-4-26B-A4B-it-QAT-MLX-4bit Locally via Ollama 2 Direct EXE Setup

Docker offers the quickest path to setting up this model locally.

Just follow the guidelines provided below.

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

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🛡️ Checksum: 3f009e7ab84136504d93308691776b84 — ⏰ Updated on: 2026-06-26 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: required: 16 GB absolute minimum for small models Disk Space: 100 GB for multi-modal model vision components Graphics: 12 GB VRAM minimum required for basic quantization

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.

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