How to Setup gemma-4-26B-A4B-it-QAT-MLX-4bit

The most rapid route to a local installation of this model is through Docker.

Make sure to follow the instructions below.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

📡 Hash Check: cdf88f6d023b036efb5f3b7c2a13bc5f | 📅 Last Update: 2026-06-26 Verify Processor: 6-core 3.5 GHz minimum required RAM: at least 32 GB in dual-channel mode for bandwidth 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.

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