Using the Windows Package Manager is the quickest way to trigger the setup.

Follow the sequence of steps detailed below.

The script takes care of fetching the multi-gigabyte model weights.

Without any user input, the software calibrates parameters for optimal hardware usage.

🔗 SHA sum: 4caa6c4af398bafbce0dace8ec291748 | Updated: 2026-06-29 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip

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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