Deploy Generative AI at the Edge Securely and Efficiently

In this whitepaper paper, we address both core challenges with adopting generative AI by introducing a novel pipeline to run LLMs on power-efficient embedded applications processors, using model optimization and RAG.

Image used courtesy of Freepik

White Paper Overview

Generative AI is creating new opportunities across industries, but organizations face two key challenges when adopting Large Language Models (LLMs): deploying them efficiently on edge devices and ensuring they can securely access private, domain-specific data. At the same time, concerns around data privacy, security, performance, and AI hallucinations continue to limit broader adoption.

This whitepaper provides a practical methodology for deploying Generative AI at the edge, including:

How to optimize LLMs for resource-constrained edge devices
Techniques for reducing model size and improving inference performance
The role of Retrieval Augmented Generation (RAG) in improving accuracy and reducing hallucinations
How to securely leverage private and domain-specific knowledge sources without retraining models
Building end-to-end AI workflows that combine LLMs with speech, audio, and other AI capabilities

Drawing on NXP’s expertise in Edge AI and embedded processing, the paper also explores:

LLM optimization through quantization and acceleration
Secure and efficient RAG implementation for private data access
The eIQ® GenAI Flow for end-to-end generative AI applications
Real-world use cases across industrial, healthcare, robotics, IoT, and automotive applications

Whether you are evaluating Generative AI for embedded systems or looking to bring intelligent, privacy-preserving AI experiences to edge devices, this resource offers practical guidance for deploying efficient, secure, and scalable AI solutions.