How to Run gemma-4-26B-A4B-it-QAT-MLX-4bit with Native FP4

For an instant local deployment, running a pre-configured shell script is ideal.

Follow the sequence of steps detailed below.

Be patient as the system self-retrieves massive model weights dynamically.

Your resources are automatically evaluated to lock in the premium configuration.

🧩 Hash sum → 29eb71a5fc0f53c07a7f340c6eeb0382 — Update date: 2026-07-01 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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