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gemma-4-26B-A4B-it-QAT-MLX-4bit Locally via Ollama 2 For Beginners

To install this model locally in the shortest time, opt for a direct curl execution.

Use the instructions provided below to complete the setup.

The download manager will automatically pull several gigabytes of data.

The smart installation system will instantly find the perfect configuration.

🔗 SHA sum: 7cd2f670cc3898cac5bff0cb75c7d801 | Updated: 2026-06-29



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: required: 16 GB absolute minimum for small models
  • Disk Space: 100 GB for multi-modal model vision components
  • 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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