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Running Large Multimodal Models on an AI PC's NPU

Community Article Published June 11, 2024
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Benjamin Consolvo's avatar
bconsolvo Benjamin Consolvo

Large multimodal models (LMMs) integrate different modes of data into AI models. These L M Ms, distinct from L L Ms, can make deductions from text, audio, video, and image data, which is a recent a paradigm shift in AI. The example I have for you here is one that uses text and image data, the llava-gemma-2b model. It is a derivative of Google's Gemma-2B and the LLaVa-1.5 model, and because of its smaller size, it can run well on a laptop.

Intel has introduced the neural processing unit (NPU) as an integrated component in its latest AI PC laptop processor - the Intel® Core™ Ultra processor. The NPU is a low power, energy efficient processor engine that elevates the game of AI model deployment on your local machine. One option for deployment to it is to use the new Intel® NPU Acceleration Library. As stated on its GitHub page, "The Intel® NPU Acceleration Library is a Python library designed to boost the efficiency of your applications by leveraging the power of the Intel Neural Processing Unit (NPU) to perform high-speed computations on compatible hardware."

Note: The Intel® NPU Acceleration Library is currently in active development, with our team working to introduce a variety of features that are anticipated to dramatically enhance performance. It is not intended as a production ready performant path but as a way to enable the developer and machine learning community to play and experiment with the NPU by prioritizing easy developer experience. For performant production ready solutions please refer to solutions like OpenVINO™ toolkit or DirectML.

How to get started with the Intel NPU Acceleration Library

In order to get up and running with the NPU on your machine, start by installing the Intel NPU Acceleration Library: 

pip install intel-npu-acceleration-library

Make sure to update to the latest NPU driver for all of the latest features and best performance:

A couple of lines of code as shown below should enable the NPU on your laptop to run small LLMs or in this case LMMs.

After beginning running your code, to see that the NPU is working, open the Task Manager program on Windows and take a look at the usage of the NPU as Figure 1 shows.

image/png Figure 1: NPU usage on AI PC shows around 63% utilization for running inference with this model.

Following and running the example found on the model card of llava-gemma-2b, we can ask "What's the content of the image?"

image/jpeg Figure 2: An example image of a stop sign (https://www.ilankelman.org/stopsigns/australia.jpg).

The model returns the following statement:

The image features a red stop sign on a

Notice that it is cut off after 30 characters. You can adjust the length that it returns by modifying the max_length=30 argument to increase the character count. You should find that inference does take longer if you increase the length.

generate_ids = model.generate(**inputs, max_length=30)

Conclusions and calls to action

I showed how to run a large multimodal model (LMM) on the NPU engine of an Intel Core Ultra AI PC processor, specifically the llava-gemma-2b model. For more technical details about this model, please visit the paper, LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model by Hinck et al. (2024) on arXiv.

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