How to Run GLM-4.5-Air-AWQ-4bit Offline Setup

The most efficient approach for a local installation is leveraging Docker containers.

Follow the sequence of steps detailed below.

Everything happens automatically, including the heavy cloud asset download.

During setup, the script automatically determines and applies the best settings.

📎 HASH: 4836c26e4d943d2800c6dc975ba09798 | Updated: 2026-07-04 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: 12 GB VRAM minimum required for basic quantization

The GLM-4.5-Air-AWQ-4bit is a compact yet powerful language model designed for both research and production environments. It leverages Activation‑aware Quantization (AWQ) to achieve high inference speed while preserving much of its original performance. With 6 billion parameters and an 8K token context window, the model can handle complex reasoning tasks and long‑form generation efficiently. The 4‑bit quantization reduces memory footprint and enables deployment on consumer‑grade hardware without noticeable loss in accuracy. Users appreciate its balanced trade‑off between size, speed, and capability, making it ideal for developers seeking a lightweight yet versatile AI assistant. Below is a quick overview of its key technical specifications.

Parameters	6 B
Context Length	8K tokens
Quantization	AWQ 4‑bit

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