Run gemma-4-26B-A4B-it-QAT-MLX-4bit PC with NPU with 1M Context Offline Setup

Running this model locally is fastest when deployed through Docker.

Use the instructions provided below to complete the setup.

After cloning, fire up the application using Docker.

📡 Hash Check: e65adfd00398ba791be0ed1496a7127a | 📅 Last Update: 2026-06-22 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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.

• Multi-threaded engine performance patch for legacy single-core games
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