Generative AI at the Edge: What You Need To Know
2025/07/24
Why Generative AI at the Edge Matters
Generative AI is transforming how we interact with machines, create content, and automate decisions. But when deployed at the edge, on local devices rather than in the cloud, it unlocks a new frontier of possibilities: ultra-low latency, enhanced privacy, offline functionality, and real-time responsiveness. For industries ranging from hospitality to industrial automation, this shift is not just evolutionary—it’s revolutionary.
Avnet Silica is at the forefront of this transformation, delivering embedded Generative AI solutions that are practical, powerful, and production-ready.
What Is Generative AI at the Edge?
Generative AI at the Edge refers to running large language models (LLMs) or multimodal models (text, vision, audio) directly on embedded devices. This enables real-time, localised AI processing without relying on cloud infrastructure. The benefits are compelling:
- Offline Capability: Operates without internet connectivity.
- Data Privacy: Keeps sensitive data on-device.
- Reduced Latency: Eliminates cloud roundtrips.
- Lower Costs: Reduces dependency on cloud APIs.
- Unique Experiences: Enables novel, context-aware interactions.
Real-World Applications
Generative AI at the Edge is transforming the world around us by enabling low-latency, more secure, and real-time AI on devices. Here are just a few real-world applications:
Hospitality
From concierge chatbots to smart room assistants, Generative AI at the Edge enhances guest experiences by enabling natural, voice-based interactions without compromising privacy.
Industrial Automation
Edge AI chatbots are transforming how operators interact with machinery. With real-time diagnostics, predictive maintenance, and voice-controlled interfaces, factories are becoming smarter and more autonomous.
Transportation
In-vehicle assistants powered by Generative AI at the Edge offer hands-free control, navigation, and infotainment without needing a cloud connection.
Overcoming the Embedded AI Challenge
Deploying Generative AI at the Edge presents a unique set of technical and operational challenges that differ significantly from traditional cloud-based AI deployments. One of the most pressing issues is the resource constraints inherent to embedded systems. Unlike cloud servers, edge devices typically have limited processing power, memory, and storage capacity. This means that large language models (LLMs), which often require gigabytes of memory and high-performance GPUs, must be significantly compressed and optimised to run efficiently on smaller, less powerful hardware.
Another critical factor is the need for real-time responsiveness. Many edge applications—such as voice assistants in vehicles or predictive maintenance systems in factories—demand ultra-low latency. Any delay in processing could compromise user experience or even safety. As a result, the AI models must be not only lightweight but also finely tuned to deliver near-instantaneous results.
Power efficiency is also a major consideration. Edge devices often operate in environments where power is limited or battery life is a concern, such as remote sensors or mobile platforms. Running complex AI models under these constraints requires careful balancing of performance and energy consumption, often necessitating the use of specialised low-power processors and hardware accelerators.
Beyond the model itself, system integration poses its own set of hurdles. Generative AI must seamlessly interface with a wide array of sensors, firmware, and embedded software stacks. This integration must be robust and reliable, especially in industrial or mission-critical settings where downtime is not an option.
Finally, there’s the challenge of model optimisation. To make LLMs viable at the edge, developers must employ advanced techniques such as quantisation, pruning, and knowledge distillation. These methods reduce the size and computational demands of the models while preserving their accuracy and functionality. It’s a delicate balancing act that requires deep expertise in both AI and embedded systems engineering.
Avnet Silica addresses these challenges through a robust ecosystem of hardware (e.g. AMD Ryzen Embedded, NXP i.MX95), software stacks, and engineering support.
What’s Next: Agentic AI and Multimodal Models
Looking ahead, the future of Generative AI at the Edge is being shaped by two major developments: Agentic AI and multimodal large language models.
Agentic AI marks a significant evolution in how artificial intelligence systems operate. Rather than simply responding to user inputs with pre-defined answers, these systems are designed to take autonomous actions based on context, intent, and learned behaviours. For example, a voice assistant could go beyond providing information about hotel availability and actually complete a booking on the user’s behalf. In industrial environments, such systems might autonomously adjust machinery settings, initiate maintenance procedures, or escalate alerts based on real-time data. This shift from reactive to proactive AI introduces new levels of automation and intelligence, enabling systems to act independently in ways that are both useful and efficient.
At the same time, the development of multimodal large language models is expanding the capabilities of AI beyond text. These models are designed to understand and generate multiple types of data, including images, audio, and even video. This opens the door to more natural and immersive interactions. For instance, a smart kiosk could interpret spoken questions, analyse visual inputs such as documents or gestures, and respond with both voice and visual content. In sectors like healthcare, retail, and transportation, this multimodal capability allows for richer, more intuitive user experiences that were previously only possible with cloud-based systems.
Together, these advancements are redefining what is possible with AI at the edge. They are enabling systems that not only understand and respond, but also perceive, reason, and act. This brings us closer to truly intelligent, context-aware machines that operate seamlessly in the real world.
Avnet Silica’s Generative AI at the Edge Projects
A standout example is Avnet Silica’s Generative AI at the Edge Chatbot. A locally hosted voice chatbot powered by a 9-billion parameter LLM. Integrated into TRIA embedded systems, it delivers fast, private, and intelligent conversations in real time.
This solution was showcased at Embedded World and electronica, where attendees interacted with a vintage-style telephone booth powered entirely by offline AI. The demo highlighted the speed, security, and creativity of embedded GenAI.
Avnet Silica’s Artificial Intelligence and Machine Learning expert, Michael Uyttersprot, then set about creating a Generative AI at the Edge podcast. This podcast allows a human to input a specific topic for the AI-generated host and expert to discuss. The human can interject and ask questions at any time.
A group of people standing next to Avnet Silica’s ‘Edge GenAI’ chatbot at Embedded World 2025.
Avnet Silica’s Role in the Edge AI Revolution
With its deep expertise, strong supplier partnerships, and proven demos, Avnet Silica is helping customers turn the promise of Generative AI at the Edge into reality. Whether it’s through the Edge GenAI chatbot, industrial deployments, or hospitality solutions, the company is enabling smarter, faster, and more secure AI experiences, right where they’re needed most.
Stop by the Avnet Silica stand at SIDO to learn more about Generative AI at the Edge, next-generation connectivity, and embedded solutions!
Avnet Silica will be present at SIDO, booth E313. Come and meet them.
