How to Setup GLM-4.5-Air-AWQ-4bit Offline on PC with Native FP4 Dummy Proof Guide

The most rapid route to a local installation of this model is through WSL2.

Refer to the action plan below to initialize the model.

All large files and heavy weights are downloaded automatically by the script.

The engine benchmarks your hardware to apply the most effective operational mode.

🔒 Hash checksum: 54b51fcbc4c24dc3b54d2eb9e7c9cd3e • 📆 Last updated: 2026-06-26 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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