gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 10

Running this model locally is fastest when deployed through Docker.

Follow the sequence of steps detailed below.

The client handles the setup, pulling gigabytes of data automatically.

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🗂 Hash: a96fe5fbcca989d35ea9f5abefa069fb • Last Updated: 2026-06-26 Verify Processor: next-gen chip for heavy context processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk: 150+ GB for high-context vector database storage GPU: high memory bandwidth GPU for next-gen local AI pipeline

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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