Artificial Intelligence

Q: Can NXP products run LLMs, VLMs, or GenAI models at the edge?

Yes, NXP enables LLM, VLM, and GenAI deployment on embedded devices through eIQ® GenAI Flow—a modular pipeline for real-time, voice-based AI interaction. Integrated NPUs deliver low-latency, energy-efficient inference, and for larger models, NXP supports discrete AI accelerator integration to meet high-throughput requirements.

Empowering the intelligent edge with AI and machine learning.

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Accelerating innovation with NXP’s edge AI and eIQ® toolkits.

NXP microcontrollers and applications processors with machine learning at the edge enable smart thermostats, industrial robotics and intelligent vehicles. Machine learning at the edge allows NXP processors to learn, adapt and make decisions. This allows NXP hardware to overcome cloud latency and bandwidth challenges. NXP edge AI processor portfolio and eIQ software toolkits enable faster responses, reduced power consumption, enhanced privacy and lower operating costs.

Artificial Intelligence Applications

Automotive

AI models deployed directly in the car enable real-time responses, enhanced privacy and security, and reduced bandwidth usage.

Industrial

Devices that observe, learn and adapt in real-time can give your business better foresight and insight, informed control and new capabilities. Adding intelligence to devices can enhance performance for products across a full spectrum of commercial applications.

Consumer

ML-enabled devices such as door locks, thermostats and lights make autonomous decisions based on user preferences and real-time data for more safety, convenience and security.

Artificial Intelligence Technologies

eIQ® Neutron Neural Processing Unit (NPU)

The eIQ® Neutron NPU is a scalable ML accelerator core, optimized for power and performance, supporting various neural networks and fully integrated with NXP's microcontrollers and processors.

Embedded Vision

Enable devices that process visual data locally at the edge, enabling real-time, private and low-latency decisions.

Voice Processing

Audio and voice processing solutions that offer high-quality, reliable voice control and communication software for low-power, small-footprint applications.

Human Machine Interface

Explore NXP’s Human-Machine Interface solutions that deliver intuitive, secure and responsive user experiences for automotive, industrial and consumer applications.

Omdia Market Radar: AI Processors for the Edge

Omdia’s report highlights Edge AI trends and names NXP a leader in processing innovation.

Read the report

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TRAINING

Artificial Intelligence Training Academy

Take your edge AI development to the next level—explore the AI and Machine Learning Academy.

Documentation

Responsible Edge AI Enablement Making AI work in the real world requires looking beyond tools and technology to ensure it is safe, reliable and responsible. That’s where Responsible AI takes center stage—bringing together technology, government and business leaders to make it a reality.	White Paper	May 5, 2025	Rev 0
eIQ GenAI Flow eIQs GenAI Flow provides building blocks like optimized large language models (LLMs) and retrieval augmented generation (RAG) to make generative AI applications accessible on edge devices.	White Paper	Oct 25, 2024	Rev 0
Intellectual Property Aspects of ML This white paper sets out to add clarity to the legal background and challenges for the IP aspects of ML for data collection and model building.	White Paper	Jul 13, 2023	Rev 1
Machine Learning at the Edge Our ebook 'Essentials of Edge Computing' shares insights into machine learning inference at the edge, such as uninterrupted processing, lower latency and user privacy.	Brochure	Jan 21, 2022	Rev 2

Explore Retrieval Augmented Generation (RAG) Methodology

eIQs GenAI Flow provides building blocks like optimized large language models (LLMs) and retrieval augmented generation (RAG) to make generative AI applications accessible on edge devices.

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FAQs

How does NXP empower edge AI development across hardware and software?

NXP accelerates edge AI through high-performance processors that integrate NPUs, GPUs, CPUs and DSPs—enabling efficient machine learning at the edge. Its unified eIQ AI Software Stack supports "bring your own model" and "bring your own data" workflows across MCUs and MPUs, while promoting privacy by processing data locally. Strategic partnerships further streamline AI integration into embedded systems.

Can NXP products run LLMs, VLMs or GenAI models at the edge?

Yes, NXP enables LLM, VLM and GenAI deployment on embedded devices through eIQ® GenAI Flow—a modular pipeline for real-time, voice-based AI interaction. Integrated NPUs deliver low-latency, energy-efficient inference, and for larger models, NXP supports discrete AI accelerator integration to meet high-throughput requirements.

What AI applications are powered by NXP processors across industries?

NXP processors support a wide range of edge AI applications:

Industrial: Machine vision, predictive maintenance, voice/gesture HMI
Smart Home & IoT: Voice assistants, behavior-aware appliances, facial recognition.
Consumer Electronics: Health wearables, smart displays, AR interfaces.
Automotive: ADAS, in-cabin monitoring, predictive maintenance, battery management.

What is the eIQ AI Software Stack and how does it support AI development?

NXP’s eIQ AI SW Stack enables flexible AI development with support for TensorFlow, PyTorch, ONNX, and BYOM/BYOD workflows. It includes tools like eIQ Time Series Studio for sensor-based AI, eIQ GenAI Flow for LLM fine-tuning, and model watermarking for IP protection. Optimized for low-latency, energy-efficient inference, it supports full pipelines for embedded vision and multimedia across MPUs and MCUs.

Does NXP offer pre-trained models and application examples for edge AI?

Yes, NXP provides a model zoo with pre-trained models for vision, audio and other domains, optimized for its MCUs and MPUs. These include conversion and fine-tuning recipes. The Application Code Hub (GoPoint) offers demonstration applications and tutorials for object recognition, face detection, voice AI, handwritten digit recognition, and LLMs—accelerating development with real-world examples.

How does NXP ensure safe, ethical and responsible AI at the edge?

NXP promotes responsible AI through a five-principle ethics framework: non-maleficence, human autonomy, explainability, vigilance and privacy-by-design. It emphasizes fairness, robustness and inclusivity, advocating for diverse datasets to prevent bias. NXP also supports AI literacy and workforce upskilling to ensure ethical deployment and long-term reliability in edge environments.

NXP Smarter World Blog

Artificial Intelligence

Accelerating innovation with NXP’s edge AI and eIQ® toolkits.

Artificial Intelligence Applications

Artificial Intelligence Technologies

Omdia Market Radar: AI Processors for the Edge

Artificial Intelligence Products

Expansive Edge AI Processor Portfolio

eIQ® AI Software Stack and Development Tools

Design Resources

Artificial Intelligence Training Academy

Hardware

Software

Additional Resources

Documentation

Explore Retrieval Augmented Generation (RAG) Methodology

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