How to Setup gemma-4-26B-A4B-it-QAT-MLX-4bit on Copilot+ PC

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

Make sure to follow the instructions below.

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

The installer diagnoses your environment to deploy the most compatible profile.

πŸ—‚ Hash: 6bc00892eda1b19da4715e8b9e0d957e β€’ Last Updated: 2026-06-28
  • Processor: Intel i7 / Ryzen 7 for heavy Quantized models
  • RAM: required: 16 GB absolute minimum for small models
  • Storage: extra room for future model updates and datasets
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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 26β€―B
Quantization 4‑bit QAT with MLX
  • Installer configuring multi-GPU tensor parallelism for large models
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  • Setup utility linking custom local LLM pipelines with federated LibreChat workspace grids
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  • Setup utility linking custom local LLM pipelines with federated LibreChat instances
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  • Downloader pulling hyper-efficient model variations tailored for mobile computing evaluation tests
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  • Script downloading lightweight models tailored for single-board computers
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  • Setup script enabling hardware-accelerated Nemotron-Mini execution on independent workstations
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