Quantizing Qwen3.5–9B to INT8: RTN vs AWQ on a Hybrid VLM Architecture

1. 1Build a hybrid VLM architecture to support quantization
2. 2Apply RTN and AWQ techniques to quantize Qwen3.5-9B to INT8
3. 3Compare the performance of RTN and AWQ on the quantized model
4. 4Configure the quantized model for deployment on various hardware platforms
5. 5Test the quantized model on benchmark datasets to evaluate its accuracy and efficiency

ML engineers and researchers working on large language models can benefit from this knowledge to optimize their models for better performance and efficiency

💡 Quantizing large language models to INT8 can significantly improve their performance and efficiency, but requires careful evaluation of different techniques like RTN and AWQ

🚀 Quantize Qwen3.5-9B to INT8 using RTN and AWQ on a hybrid VLM architecture 🤖