How to Launch GLM-4.5-Air-AWQ-4bit on AMD/Nvidia GPU No-Internet Version

Running this model locally is fastest when deployed through a PowerShell script.

Just follow the guidelines provided below.

The engine will automatically fetch large dependencies in the background.

The automated script takes care of everything, tailoring the setup to your specs.

🧮 Hash-code: f5ddb3f52b08fae020148b38479ff7dc • 📆 2026-07-09



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: 100 GB for multi-modal model vision components
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The GLM-4.5-Air-AWQ-4bit is a cutting-edge language model that seamlessly balances research and production capabilities, making it an ideal choice for developers seeking a lightweight yet versatile AI assistant. Its Activation-aware Quantization (AWQ) technology enables high inference speed while preserving much of its original performance. With 6 billion parameters and an 8K token context window, the model can efficiently handle complex reasoning tasks and long-form generation. This results in improved accuracy without significant increases in memory footprint or computational requirements. The 4-bit quantization further enhances deployment flexibility on consumer-grade hardware. As a result, users appreciate its balanced trade-off between size, speed, and capability.

  • The model’s parameters are carefully optimized to ensure efficient inference while maintaining high performance.
  • AWQ technology allows for significant reduction in memory footprint without compromising accuracy.
  • The 8K token context window enables the model to capture nuanced contextual relationships, leading to improved long-form generation capabilities.
Total Parameters 6 billion
Context Window Length 8K tokens
Quantization Type AWQ 4-bit

Achieving a Balance between Performance and Efficiency

The GLM-4.5-Air-AWQ-4bit’s unique architecture allows it to achieve an optimal balance between performance, efficiency, and capability. This makes it an attractive choice for developers seeking to deploy AI models on consumer-grade hardware without sacrificing accuracy.

Technical Specifications at a Glance

Parameter Count 6 billion
Token Context Window Length 8K tokens
Quantization Method Activation-aware Quantization (AWQ) 4-bit

The GLM-4.5-Air-AWQ-4bit is a powerful tool for developers seeking to create efficient and accurate AI models. Its unique combination of features makes it an ideal choice for research, development, and production environments.

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