Setup GLM-4.5-Air-AWQ-4bit with 1M Context

To get this model running locally in no time, utilize the built-in WSL tools.

Please follow the instructions listed below to get started.

The process automatically pulls down gigabytes of critical model assets.

The configuration wizard runs silently to set up the model for peak performance.

📎 HASH: 3f1b5364086d0c64a7492ad2643071fa | Updated: 2026-07-02 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 100 GB for multi-modal model vision components GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

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.

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