Quick Run gemma-4-26B-A4B-it-QAT-MLX-4bit Full Speed NPU Mode

Deploying locally takes the least amount of time when executed through native OS tools.

Refer to the instructions below to proceed.

The setup auto-downloads all needed files (several GBs).

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔧 Digest: c528b02d9e98c768a18c63aabac806fb • 🕒 Updated: 2026-07-03
  • Processor: high single-core performance needed for token latency
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: 100 GB for multi-modal model vision components
  • 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.

Parameters 26 B
Quantization 4‑bit QAT with MLX
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