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blog/openvino_vlm/openvino-vlm.md

@@ -138,13 +138,18 @@ Quantizing activations adds small errors that can build up and affect accuracy,
 You can now run inference with your quantized model :
 
 ```python
-# Generate outputs with quantized model
 generated_ids = q_model.generate(**inputs, max_new_tokens=500)
 generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
 print(generated_texts[0])
 ```
-Try the complete notebook [here](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/vision_language_quantization.ipynb).
 
+If you have a recent Intel laptop, Intel AI PC, or Intel discrete GPU, you can load the model on GPU by adding `device="gpu"` when loading your model:
+
+```python
+model = OVModelForVisualCausalLM.from_pretrained(model_id, device="gpu")
+```
+
+Try the complete notebook [here](https://github.com/huggingface/optimum-intel/blob/main/notebooks/openvino/vision_language_quantization.ipynb).
 
 ## Conclusion