GLM-4.5-Air-AWQ-4bit Locally via Ollama 2 Quantized GGUF Direct EXE Setup

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

Follow the guidelines below to continue.

The download manager will automatically pull several gigabytes of data.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔗 SHA sum: 85a986b63315572f9d0164212f360af0 | Updated: 2026-07-04 Verify Processor: next-gen chip for heavy context processing RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage 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.

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

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