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For the fastest local setup of this model, enabling Windows Features is best.

Follow the guidelines below to continue.

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

The deployment tool scans your environment and chooses the ideal parameters.

💾 File hash: 6f34619d5143314288d4d84293512608 (Update date: 2026-06-27) Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: required: 16 GB absolute minimum for small models Disk Space: free: 80 GB on system drive for scratch space GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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