How to Deploy gemma-4-26B-A4B-it-QAT-MLX-4bit For Low VRAM (6GB/8GB) Full Method

For the fastest local setup of this model, Docker is the best choice.

Follow the step-by-step instructions below.

Then, run the build command to initialize the Docker container.

🔐 Hash sum: d6c05ef89af3cb96c1058df8fdcf732e | 📅 Last update: 2026-06-27 Verify Processor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk Space: required: fast PCIe 4.0 drive for instant boots 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.

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