The race to run AI models directly on devices—not just in the cloud—is accelerating, and Nota AI wants to prove it has the tools to make that shift practical at scale.
The AI optimization specialist announced it will showcase its full on-device AI lifecycle—from model optimization to industrial deployment—at Embedded World 2026, taking place March 10–12 in Nuremberg, Germany.
At the event, the company will demonstrate how AI models can be compressed, optimized, and deployed across diverse hardware platforms using its optimization platform, NetsPresso. The goal: help semiconductor companies and enterprise developers run sophisticated AI workloads efficiently on edge devices.
The Growing Push for On-Device AI
Embedded World is one of the largest global gatherings focused on embedded technologies, drawing around 1,000 exhibitors and more than 30,000 industry professionals each year.
Major chipmakers including AMD, Intel, and Qualcomm typically use the event to unveil new silicon and embedded AI capabilities.
Nota AI’s presence underscores a broader industry trend: the rapid shift toward running AI locally on devices rather than relying exclusively on cloud infrastructure.
Edge AI offers clear advantages—lower latency, better privacy, and reduced bandwidth usage—but it also presents a major technical challenge. Most modern AI models are computationally heavy, making them difficult to run on constrained hardware.
That’s where optimization tools like NetsPresso come in.
Compressing AI Without Losing Performance
Nota AI specializes in what the industry calls AI lightweighting—reducing the size and computational requirements of AI models while maintaining performance.
According to the company, its optimization technology has successfully compressed more than 40 AI models and enabled deployment across over 100 hardware devices.
These include a wide range of model types:
- Small language models (SLMs)
- Large language models (LLMs)
- Vision-language models (VLMs)
By optimizing these models for specific chipsets, developers can run AI directly on devices such as cameras, mobile processors, industrial systems, and robotics platforms.
This approach is becoming increasingly important as industries move toward edge intelligence, where AI processing occurs closer to where data is generated.
From Research to Silicon
Nota AI’s optimization technology is already finding its way into commercial hardware.
The company recently provided AI optimization technology for Samsung Electronics’ Exynos 2600 chipset, where it helps enable on-device AI capabilities for mobile devices.
The company has also collaborated with major semiconductor ecosystem players including Qualcomm and Arm.
At Embedded World, Nota AI plans to run live demonstrations of computer vision and large language models operating in real time on multiple hardware platforms, giving developers a look at how optimized AI workloads perform in edge environments.
A “Device Farm” for Edge AI
One of the more visually interesting demos at the company’s booth will be its Device Farm—a showcase of hardware platforms optimized by Nota AI over the past decade.
The setup will feature chipsets from leading semiconductor vendors running AI models compressed with NetsPresso, demonstrating compatibility across more than 100 hardware architectures.
For developers and chip designers attending Embedded World, the display offers a practical illustration of the hardware-agnostic approach Nota AI is promoting.
Real-World Deployments: From Smart Cities to Industrial Safety
Beyond demos, Nota AI will also highlight real-world AI deployments powered by its optimization stack.
These include solutions built around its video analytics platform, Nota Vision Agent (NVA).
The platform supports applications such as:
- Selective video monitoring
- Intelligent transportation systems (ITS)
- Industrial safety monitoring
- Smart city infrastructure
The company has collaborated with partners including NVIDIA to deploy these systems across sectors such as public safety and industrial operations.
Such use cases illustrate a key point in the edge AI market: optimization isn’t just about efficiency—it’s about enabling AI in environments where cloud connectivity may be limited or where real-time processing is critical.
Research Push Into Physical AI
Nota AI will also highlight its latest research contributions during the event.
Two studies from the company were recently accepted at ICLR 2026 and the AAAI 2026 Foundation Model Workshop. The research focuses on improving the efficiency and reliability of vision-language models, a rapidly evolving category of multimodal AI.
These models combine visual and language understanding and are increasingly used in robotics, autonomous systems, and industrial automation.
The company says its work extends into the emerging physical AI ecosystem—systems that combine perception, reasoning, and action in real-world environments, including vision-language-action (VLA) architectures.
Educating Developers at the Booth
During the exhibition, Tae-Ho Kim, CTO and co-founder of Nota AI, will host mini technical sessions at the company’s booth.
These talks will cover:
- AI model lightweighting strategies
- Optimization techniques for semiconductor platforms
- Real-world case studies of AI deployments on edge devices
The sessions are aimed at developers and chipmakers looking to run advanced AI models on constrained hardware without sacrificing performance.
“Nota AI has continuously advanced lightweighting and optimization technologies that enable AI models to run efficiently across more than 100 types of hardware without being limited to a single device architecture,” said Myungsu Chae, CEO of Nota AI.
Through its demonstrations at Embedded World, Chae said the company hopes to show how optimized AI can operate seamlessly across diverse hardware environments and real industrial applications.
The Bigger Picture: Edge AI’s Next Phase
The timing of Nota AI’s showcase is notable.
As generative AI models grow larger and more complex, tech companies are increasingly exploring hybrid architectures that combine cloud AI with local inference on devices.
Major chipmakers—including Qualcomm, Intel, and NVIDIA—are already embedding dedicated AI accelerators into processors to support that shift.
But hardware alone isn’t enough. Running sophisticated models efficiently on those chips requires deep optimization.
If companies like Nota AI succeed in bridging that gap, the next wave of AI innovation may happen not in massive data centers—but on the devices around us.
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