EXL2

GLM-4.5-Air-AWQ-4bit on Your PC Zero Config Full Method

GLM-4.5-Air-AWQ-4bit on Your PC Zero Config Full Method

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

Refer to the action plan below to initialize the model.

1-click setup: the app automatically fetches the large weight files.

The setup file includes a feature that instantly optimizes all configurations.

🛡️ Checksum: f234401d8d530297f1264f595c521e7e — ⏰ Updated on: 2026-07-10



  • Processor: 6-core 3.5 GHz minimum required
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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 Parameters6 billion
Context Window Length8K tokens
Quantization TypeAWQ 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 Count6 billion
Token Context Window Length8K tokens
Quantization MethodActivation-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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