The embedded industry is moving quickly, and embedded world North America 2026 is where many of those changes will come into focus. Taking place September 22–24 at the Anaheim Convention Center, the event brings the embedded community together to see how emerging technologies are moving from development into real-world deployment.
Premio will be at Booth #6100 with new solutions and live demos that reflect where embedded computing is headed. Ahead of the show, we’re highlighting four key technology trends worth watching, covering the shift toward Physical AI, more powerful computing at the edge, deeper IT/OT integration and new ways to remotely manage edge systems at scale.
Let’s start with one of the biggest shifts we expect to see at the show: Physical AI.
1. Physical AI Is Moving Intelligence Into the Physical World
Physical AI brings artificial intelligence into machines that can perceive the physical world, understand what is happening around them and take action in real time. It is becoming a key technology behind autonomous mobile robots (AMRs), robotic arms, humanoids and other autonomous systems that need to operate with greater independence.
Unlike AI workloads that can rely primarily on cloud computing, Physical AI requires processing close to where data is generated. Cameras and sensors continuously capture information about the surrounding environment, while embedded edge computers process this data locally for computer vision, AI inference, sensor fusion and autonomous navigation. This reduces cloud latency and allows machines to respond to changing conditions in real time, even when network connectivity is limited.
As autonomous systems take on more complex tasks, embedded computing requirements increase as well. A single system may need to process multiple camera streams and AI models while communicating directly with sensors and machine controls. This places greater demand on computing performance, AI acceleration and I/O connectivity within the embedded system.
One development to watch as Physical AI advances is how the software ecosystem is evolving to support more capable autonomous systems at the edge. NVIDIA JetPack 7.2 reflects this direction, with updates designed to support higher AI performance and increasingly complex edge AI workloads on Jetson platforms.
What’s New in NVIDIA JetPack 7.2
JetPack 7.2 extends the JetPack 7 software foundation to the Jetson Orin family, bringing Orin and Thor onto a common foundation with Ubuntu 24.04 and CUDA 13. Key updates include:
- AGX Orin 32GB Super Mode: Increases AI performance from 200 TOPS to up to 241 TOPS, providing more compute headroom for demanding inference workloads.
- NVIDIA Agent Skills: Supports AI-assisted workflows for system diagnostics, model benchmarking, memory optimization and deployment configuration.
- NemoClaw integration: Enables one-command deployment for agentic AI workflows that combine AI models with local data sources and tool execution.
- Official Yocto support: Gives embedded developers greater control over custom Linux distributions for production Jetson deployments.
At embedded world North America 2026, Premio will showcase JetPack 7.2 running on our NVIDIA Jetson-based edge AI computers. See the latest Physical AI capabilities in action at Booth #6100, or book a meeting with our team.
2. Edge AI Is Driving the Shift to Accelerated Computing
With more intelligence now running directly on embedded systems, edge AI workloads are becoming larger and more compute-intensive. These demands are pushing embedded computing beyond traditional CPU-only architectures.
One trend to watch is the growing use of heterogeneous computing, where different processors work together based on the type of workload. CPUs handle general-purpose computing and system control, GPUs provide parallel processing for compute-intensive AI workloads, while NPUs add dedicated acceleration for efficient AI inference.
The right computing architecture depends on the complexity of the edge AI workload:
- CPU + NPU: Well suited for lighter AI inference where power efficiency and integrated acceleration are priorities.
- CPU + integrated GPU: Provides additional parallel processing for moderate vision and AI workloads without requiring a discrete graphics card.
- CPU + discrete GPU: Designed for more compute-intensive workloads such as high-resolution machine vision, multi-camera analytics and larger AI models.
For the most compute-intensive edge AI workloads, discrete GPUs provide higher levels of acceleration to meet demanding performance requirements.
Bringing NVIDIA GPU Acceleration to the Edge
Premio is bringing the latest GPU acceleration to the industrial edge with support for NVIDIA RTX and RTX PRO GPUs, including the latest RTX PRO Blackwell generation. These GPUs expand the level of AI performance available in industrial computing systems, enabling more demanding workloads to be processed directly at the edge.
- Latest NVIDIA GPU architectures: Support for NVIDIA RTX PRO Blackwell GPUs, with configurations ranging from the RTX PRO 2000 Blackwell to the RTX PRO 6000 Blackwell Max-Q Workstation Edition.
- High-performance GPU expansion: Select Premio solutions support full-height, full-length GPUs, including dual-GPU configurations for more compute-intensive AI workloads.
- Up to 600W GPU power budget: Premio’s VCO-6000 Series supports dual FHFL GPUs with up to a 600W GPU power budget for demanding edge AI deployments.
As edge AI workloads become more complex, this level of GPU acceleration reflects a broader shift in embedded computing toward heterogeneous, AI-accelerated systems built to process more intelligence directly at the edge.
Explore Premio’s Industrial GPU Computers >>
3. IT/OT Convergence Is Driving More Flexible Industrial Connectivity
The line between information technology (IT) and operational technology (OT) continues to narrow as industrial data becomes more important to connected operations. Data generated by machines, PLCs and sensors can now feed into monitoring systems, analytics platforms and other enterprise applications. Embedded edge computers help bridge these environments by collecting and processing operational data close to the source while connecting factory-floor equipment with higher-level IT systems.
But bringing IT and OT together creates a practical connectivity challenge: industrial environments often include equipment from different generations, each with its own interfaces and communication requirements. One system may need to connect legacy machines through serial ports, IP cameras over Ethernet or PoE, sensors through digital I/O, plus newer devices that require high-speed networking or wireless connectivity.
This is making flexible industrial I/O an increasingly important part of embedded system design. Depending on the deployment, an edge computer may need to support Ethernet, 10GbE, PoE, M12, USB, RS-232/422/485, digital I/O or wireless expansion. The ability to adapt these connections also helps integrators support existing OT equipment while introducing newer IT infrastructure without redesigning the entire computing system.
Expanding Industrial Connectivity with EDGEBoost I/O
Premio addresses these changing connectivity requirements with EDGEBoost I/O, a modular I/O architecture that allows compatible industrial computers to add or combine connectivity based on the needs of the deployment.
- Industrial networking: Expand connectivity with RJ45 or M12 Ethernet, PoE and 10GbE.
- Machines and peripherals: Add USB, RS-232/422/485 or isolated digital I/O for different industrial devices.
- Edge AI and wireless expansion: Add M.2 support for NVMe storage, AI accelerators or 5G connectivity.
This modular approach reflects a broader shift in industrial computing: IT and OT are becoming more connected, increasing the need for edge systems that can support both existing industrial equipment and emerging technologies within the same infrastructure.
Explore Modular EDGEBoost I/O Technology >>
4. Distributed Edge Deployments Are Making Remote Management Essential
Embedded and edge systems are increasingly deployed across production sites, transportation systems, smart infrastructure and other locations where physical access can be difficult or costly. As these deployments grow, maintaining every device onsite becomes harder to scale.
This is making remote management an increasingly important part of long-lifecycle edge deployments. For organizations operating industrial computers across distributed locations, remote management provides a way to monitor system health, troubleshoot issues and perform maintenance without requiring physical access to every device. This can help organizations:
- Reduce onsite service visits and TCO by resolving more issues remotely.
- Improve system uptime through continuous monitoring and faster response.
- Accelerate troubleshooting and recovery when devices experience failures.
- Support long deployment lifecycles with ongoing visibility and maintenance.
Keeping Distributed Edge Systems Connected and Recoverable
Premio brings remote management directly to the edge through two complementary approaches: In-Band and Out-of-Band (OOB) management.
|
|
In-Band |
Out-of-Band (OOB) |
|
Access |
Through the OS and software agent |
Independent of the OS |
|
Primary Role |
Monitoring and device management |
Hardware-level access and recovery |
|
Availability |
When the OS is operational |
Even when the OS is unresponsive |
During normal operation, In-Band management provides the visibility needed to monitor and maintain edge devices. But when the operating system freezes or becomes unresponsive, that software-based connection may no longer be available. OOB management provides a separate hardware-level path to regain access and recover the system remotely.
Premio supports both approaches through our own hardware-level OOB solution and our partnership with Allxon:
- Premio EDGEBoost OOB: Premio’s hardware-level OOB solution provides remote access through a dedicated management interface. Using the Premio OOB management GUI, operators can remotely control power, schedule power cycles, check device status and access the serial console without relying on the operating system.
- Allxon Integration: Through our partnership with Allxon, Premio supports both In-Band and OOB management for deployments that require broader device monitoring and fleet management, including alerts, log collection, GPU monitoring, OTA updates and fleet provisioning.
Explore Premio’s Remote Management Solutions >>
Meet Premio at embedded world North America 2026
From Physical AI and accelerated computing to flexible industrial connectivity and remote management, these four trends point to a broader shift in embedded computing: more intelligence is moving to the edge, and the systems behind it are becoming more capable, connected and manageable.
At embedded world North America 2026, Premio is bringing these technologies to life at Booth #6100. Meet our team, explore our latest rugged edge computing solutions and see live demonstrations of AI running at the edge.
See Live Edge AI Demos at Booth #6100
- Cybersecure AI at the Edge | Intel Metro AI Suite + Exein Runtime: See real-time traffic, pedestrian and congestion analytics paired with runtime protection for connected edge AI workloads, demonstrating how AI performance and cybersecurity can come together at the edge.
- NVIDIA NemoClaw | JCO-1000-ORN Series: See agentic AI running locally on NVIDIA Jetson Orin, demonstrating how AI agents can operate at the edge with reduced cloud dependency.
- Live Vision-Language AI | JCO-6000-ORN Series: Experience real-time visual understanding and natural language interaction, bringing multimodal AI capabilities to robotics, machine vision and Physical AI applications.
Ready to see what’s next for embedded computing? Register for embedded world North America 2026. We can’t wait to meet you in Anaheim!