How to Run GLM-4.5-Air-AWQ-4bit One-Click Setup 5-Minute Setup

If you want the fastest local installation for this model, use standard pip packages.

Go through the configuration rules shown below.

Hands-free setup: the system self-downloads the heavy model files.

The initial setup handles the heavy lifting, fine-tuning the environment for your device.

🔐 Hash sum: ad47af820fa3bcb7cd0e7cc6fd4c3418 | 📅 Last update: 2026-07-07
  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space:70 GB free space for full FP16 weights storage
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

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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