Run gemma-4-26B-A4B-it-QAT-MLX-4bit PC with NPU

Running this model locally is fastest when deployed through Docker.

Refer to the instructions below to proceed.

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

🧩 Hash sum → d39016a73dce607a2cc765830c4e76a6 — Update date: 2026-06-24 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 48 GB needed to prevent memory swapping to disk Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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.

1. Offline bot skirmish mode activator for competitive multiplayer tactical games
2. Setup gemma-4-26B-A4B-it-QAT-MLX-4bit One-Click Setup FREE
3. Simultaneous client sandbox loader for operating multiple accounts locally
4. gemma-4-26B-A4B-it-QAT-MLX-4bit Windows 10 For Low VRAM (6GB/8GB) No-Code Guide
5. Asset archive unpacker tool for extracting high-quality game sounds and models
6. Run gemma-4-26B-A4B-it-QAT-MLX-4bit Locally (No Cloud) Zero Config