Deploying locally takes the least amount of time when executed through native OS tools.
Make sure to follow the instructions below.
The engine will automatically fetch large dependencies in the background.
The setup file includes a feature that instantly optimizes all configurations.
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 |
- Installer enabling token streaming and localized generation logging
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- Script fetching custom model merges directly into specific KoboldAI directory trees
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- Downloader for ChatRTX library updates containing multi-folder file indexing layers
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