How to Autostart gemma-4-26B-A4B-it-QAT-MLX-4bit on AMD/Nvidia GPU with Native FP4 Direct EXE Setup

How to Autostart gemma-4-26B-A4B-it-QAT-MLX-4bit on AMD/Nvidia GPU with Native FP4 Direct EXE Setup

The shortest path to running this model is by activating Hyper-V features.

Follow the sequence of steps detailed below.

The download manager will automatically pull several gigabytes of data.

There is no manual tuning required; the builder deploys the best matching configuration.

💾 File hash: abf8159057346fdbcc6623afff5a6f1a (Update date: 2026-07-06)
  • Processor: next-gen chip for heavy context processing
  • RAM: required: 16 GB absolute minimum for small models
  • Storage: extra room for future model updates and datasets
  • 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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