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

Deploying this model locally is quickest when done via a simple curl command.

Check out the detailed setup guide below to begin.

The installer automatically pulls the model (could be multiple GBs).

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

📘 Build Hash: fc074eccc69031f377facb23b6ba979f • 🗓 2026-06-28 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models Disk: high-speed SSD 120 GB to cache model layers GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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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