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AI & Machine Learning

What hardware and software requirements are needed for edge AI deployments?

Chris Bentley 3 July 2025

hardware and software requirements for edge AI

Edge AI is changing the way industries work. By bringing artificial intelligence closer to where data is generated, whether that’s on a factory floor, in a hospital, or at a retail checkout, it powers faster decisions and sharper insights. But let’s be clear: success with edge AI is about picking the right hardware and software to handle the unique demands of edge environments.

It’s what Simply NUC does. Our compact, powerful systems are built for exactly these kinds of challenges, ready to deliver reliable, secure performance at the edge.

Hardware requirements for Edge AI deployments

Processing power

Edge AI needs serious processing muscle. AI workloads depend on CPUs, GPUs, and sometimes dedicated AI accelerators to handle tasks like real-time image recognition, predictive analytics, and natural language processing.

Simply NUC’s extremeEDGE Servers™ and Onyx systems are designed with this in mind. Whether you’re running complex models on-site or supporting AI inferencing at remote locations, these devices pack scalable power into compact footprints.

Picture a manufacturing facility using high-performance edge technology for predictive maintenance. The system crunches sensor data on the fly, spotting trouble before machines fail, and saving big on downtime costs.

Storage capacity

Edge AI generates and works with large amounts of data. Fast, reliable storage is essential to keep things moving. High-capacity SSDs deliver low-latency access, helping systems store and retrieve data without slowing down operations.

For example, smart checkout stations in retail environments rely on local storage to hold transaction data securely until it can sync with central servers, especially critical when connections are spotty.

Connectivity options

No edge AI system is an island. It needs robust connectivity to link up with sensors, other edge nodes, and enterprise systems. Think 5G, Wi-Fi 6, Ethernet, or low-power options like Bluetooth, each plays a role depending on the use case.

In healthcare, edge AI devices that process patient vitals require secure, always-on connections. When lives are at stake, data needs to flow without a hitch.

Robust security features

Edge devices often handle sensitive data locally. That means security can’t be optional. Built-in protections like secure boot, encryption modules, and tamper-resistant designs are critical to keep systems safe from physical and digital threats.

Consider a financial institution using edge AI for fraud detection. Encryption-enabled systems protect transaction data in real time, guarding against breaches while meeting compliance requirements.

Ruggedness and durability

Edge environments aren’t always friendly. Devices might face dust, heat, vibration, or moisture, sometimes all at once. Rugged enclosures and industrial-grade components help hardware thrive in these conditions without constant maintenance.

Environmental organizations are a prime example of this. Their edge systems need to stand up to harsh elements while continuously processing geological data and safety metrics.

Scalability

Edge AI deployments often start with a few devices and grow over time. That growth needs to happen without replacing everything. Modular hardware, with PCIe expansion, makes it easy to scale processing, storage, or connectivity as needs evolve.

A logistics company scaling up its delivery network, for example, can add capacity to its edge AI systems as more vehicles and routes come online, no rip-and-replace required.

Software requirements for Edge AI deployments

AI frameworks

Your AI models need the right frameworks to run efficiently at the edge. These frameworks are designed to squeeze the most out of limited resources without compromising performance.

TensorFlow Lite, PyTorch Mobile, and Intel’s OpenVINO Toolkit are popular picks. They help deploy lightweight models for fast, local inference.

Picture logistics drones using TensorFlow Lite for object detection as they navigate warehouses, fast, accurate, and all done locally without relying on the cloud.

Operating systems

Edge AI hardware needs an OS that can keep up. Linux-based systems are a go-to for flexibility and reliability, while real-time operating systems (RTOS) are vital for applications where every millisecond counts.

Think of healthcare robotics. These systems depend on RTOS for precise control, whether it’s guiding a surgical tool or monitoring vitals during an operation.

AI model optimisation tools

Edge devices can’t afford bloated AI models. That’s where optimization tools like ONNX Runtime and TensorRT come in. They fine-tune models so they run faster and leaner on edge hardware.

For example, retail automation systems might use these tools to speed up facial recognition at checkout stations, helping to keep lines moving without breaking a sweat.

Device management tools

Edge AI deployments often involve fleets of devices spread across locations. Centralised management tools like Kubernetes, Azure IoT Hub, or AWS IoT Core let teams update firmware, monitor performance, and roll out new features at scale.

A factory managing hundreds of inspection cameras can use Azure IoT Hub to push updates or tweak settings without touching each device manually.

Security software

Software security is just as crucial as hardware protections. Firewalls, intrusion detection systems, identity and access management (IAM), these keep edge AI systems safe from cyber threats.

Financial firms, for instance, rely on IAM frameworks to control who can access edge systems and data, locking down sensitive operations against unauthorised use.

Analytics and visualisation tools

Edge AI generates valuable data, but it’s only useful if you can make sense of it. Tools like Grafana and Splunk help teams see what’s happening in real time and act fast.

Retailers use these platforms to map customer flow through stores, spotting patterns that help fine-tune layouts and displays on the fly.

Tailoring requirements to industry-specific use cases

The right mix of hardware and software depends on your world.

In healthcare, security and reliable connectivity take priority, think patient privacy and real-time monitoring.
In manufacturing, ruggedness and local processing power rule, factories need systems that survive harsh conditions and make decisions on-site.
In retail, connectivity and scalability shine, smart shelves, checkouts, and analytics thrive on flexible, connected edge gear.

Simply NUC’s customizable hardware options make it easier to match solutions to these diverse needs, whether you’re securing a hospital network or scaling up a retail operation.

Edge AI’s potential is huge, but only if you build it on the right foundation. Aligning your hardware and software with your environment, use case, and goals is what turns edge AI from a cool idea into a real driver of value.

Simply NUC’s purpose-built edge solutions are ready to help, compact, scalable, and secure, they’re designed to meet the demands of modern edge AI deployments.

Curious how that could look for your business? Let’s talk.

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AI & Machine Learning

What is the ROI of implementing edge AI solutions, and how do we measure success?

Chris Bentley

Thanks to edge computing, artificial intelligence is working right where data is being created; on devices at the edge of your network. This means faster decisions, less lag, and smarter operations without always leaning on the cloud.

The big question for any business eyeing this tech? What’s the return on investment, and how do you know if you’re getting it? Let’s break it down, with a focus on practical strategies to get the most out of your edge AI deployments.

The business case for Edge AI

Edge AI gives companies a serious edge (pun intended) in their operations. It helps cut costs, boost efficiency, delight customers, and stay ahead of competitors.

Picture predictive maintenance on a factory line, machines flag issues before they break down. Or quality control that spots defects in milliseconds. In retail, smart inventory systems keep shelves stocked without over-ordering. This represents real savings in money and time.

What to consider before jumping in

Edge AI isn’t a one-size-fits-all solution. To get a solid ROI, it has to tie back to your business goals.

Start by asking: What problems are we solving? Which KPIs matter most? Whether it’s cutting downtime or speeding up delivery times, clarity here pays off.

Your existing infrastructure matters too. Can it support edge AI, or will you need upgrades? Factor in integration costs and think through risks like data management complexity or cybersecurity gaps. A smart mitigation plan upfront helps avoid headaches down the line.

How to build a smart Edge AI strategy

Getting ROI from edge AI doesn’t happen by accident. Success starts with clear KPIs, ones that match your broader strategy. From there, build a detailed plan: timelines, budgets, resources. Governance matters too. Who’s steering the ship? How will you handle compliance, data policies, and tech updates?

Flexibility is key. The hardware and software you choose should scale with your business and adapt as needs shift. That’s where solutions like Simply NUC’s extremeEDGE servers shine. They’re built to handle rugged environments, remote management, and future expansion without breaking a sweat.

Measuring and maximizing ROI

So how do you actually measure success? Here’s where to look:

Cost savings

Edge AI reduces cloud dependence, slashing storage and bandwidth bills. Plus, fewer outages and smarter resource use add up.

Measure it:

Compare cloud costs before and after rollout
Track savings from fewer disruptions or manual interventions
Track ongoing running costs

Operational efficiency

Edge AI automates repetitive tasks and sharpens decision-making. Your processes move faster, with fewer errors.

Measure it:

Time saved on key workflows
Productivity metrics pre- and post-deployment
Latency improvements that speed up operations

Customer experience

Real-time AI means quicker responses and personalized service. That builds loyalty.

Measure it:

Customer satisfaction survey results
Changes in Net Promoter Score (NPS) or retention
Engagement metrics, like faster response times or higher usage

Reliability and uptime

Edge AI helps spot trouble early, keeping systems running.

Measure it:

Downtime logs before and after deployment
Revenue or production saved through increased uptime

Scalability

Edge AI should grow with you, supporting more devices and data without blowing up costs.

Measure it:

Compare cost per unit as your system scales
Assess how smoothly the system handles added workloads

Data and infrastructure: the foundation for ROI

None of this works without solid data management. Edge AI needs accurate, secure, real-time data to do its job. That means having strong data governance and compliance baked in.

On the infrastructure side, look for scalable, reliable, secure edge computing hardware that matches your needs. Total cost of ownership matters here too, cheap upfront doesn’t help if maintenance or downtime costs pile up later.

Edge AI can absolutely deliver measurable business results, from saving money and time to creating better experiences for your customers. But like any tech investment, ROI depends on getting the strategy right.

When you align edge AI with your goals, build a plan that fits your business, and choose infrastructure that’s ready to scale, you set yourself up for success.

Curious where edge AI could take your business? Let’s chat about what would work best for your team. Contact us today.

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AI & Machine Learning

How is data stored and processed at the edge?

Jonny Cameron 1 July 2025

If you’re in an industry that can’t afford to wait for data to be stored and processed, then edge computing should be playing a significant role in your IT infrastructure.

Think factories, hospitals, logistics networks, places where decisions need to happen in real time, not after a round trip to some distant data center. Storing and processing data at the edge keeps information close to where it’s created and used. That means faster insights, lower latency, and tighter security.

From solid-state drives that thrive in tough conditions to smart distributed systems and hybrid setups that blend cloud convenience with local control, businesses now have options that match the realities of their environments.

Key methods of data storage at the edge

Solid-state drives (SSDs)

SSDs are the workhorse of edge storage. Unlike traditional spinning hard drives, SSDs use flash memory, which means faster read and write speeds, no moving parts to break, and much better durability. That’s a big deal when devices are sitting on vibrating machinery or exposed to temperature swings.

Here’s the payoff: with SSDs, edge devices can process data in real time. A manufacturing plant, for example, might use SSD-equipped edge servers to capture and analyze sensor data from equipment. That data helps predict maintenance needs, so teams can fix small issues before they turn into expensive breakdowns.

Another plus? SSDs come in compact form factors, which makes them perfect for tight spaces where every inch counts.

Distributed storage systems

When data is spread across multiple edge devices instead of being funneled to a central server, you get what’s called distributed storage. It’s like creating a mini network that stores and processes data locally at each site.

Why is that helpful? If one device goes offline, maybe for maintenance or because of a connection issue, the others keep the system running. That resilience makes distributed storage ideal for industries like retail, where individual locations need to function independently.

Imagine a retail chain where each store has its own edge storage. The system lets stores process transactions, manage inventory, and even run localized promotions without waiting on the main office or cloud. When the connection’s good, everything syncs. When it’s not, the store keeps moving without missing a beat.

Hybrid cloud-edge storage

Hybrid storage gives businesses the best of both worlds. Critical data that needs fast access stays on edge devices. Data that’s less time-sensitive or used mainly for historical analysis can live in the cloud.

This setup helps balance performance, cost, and flexibility. Take healthcare, for example. Real-time patient monitoring data stays at the edge so that vitals can be analyzed instantly. But once that data is no longer immediately relevant, it gets archived in the cloud where it can be retrieved if needed.

The result? Less network congestion, lower latency, and the ability to scale storage as needed without overloading local devices.

How edge storage methods enhance operations

Storing and processing data at the edge isn’t just a technical choice, it delivers real, measurable benefits that drive better business outcomes.

Improved performance

When data stays close to its source, systems can act on it faster. There’s no need to send information back to a distant data center or cloud server and wait for a response. This speed boost is crucial in environments like manufacturing, where split-second decisions keep production lines running smoothly, or in logistics, where real-time tracking ensures deliveries stay on schedule.

Reduced latency

Latency is the enemy of real-time operations. Every millisecond counts in sectors like healthcare or finance, where delays can have serious consequences. By storing and processing data locally, edge solutions slash latency because they cut out the long round trips to cloud systems.

In a hospital where patient monitors equipped with edge AI process vitals right there in the room. Doctors and nurses get instant alerts if something goes wrong, no waiting for data to travel to and from a central server.

Enhanced security

Sending data over networks always introduces risk. The less data that travels long distances, the fewer chances there are for it to be intercepted or tampered with. Edge storage keeps sensitive information, like personal health records or financial transactions, local and protected.

This is where Simply NUC’s compact, high-performance edge devices come into their own. Built for tough environments and tight spaces, their systems pack serious processing power and secure storage into small, rugged packages. That means you can deploy them on factory floors, in remote retail locations, or out in the field, wherever your edge operations need to be.

Solid-state drives, distributed storage systems, and hybrid cloud-edge models aren’t competing options, they’re often part of the same solution. Together, they help businesses store data where it makes the most sense: close to where it’s created, easy to access, and protected from threats.

By choosing hardware that’s built for the realities of edge environments, like Simply NUC’s scalable, secure devices, you can be confident your storage infrastructure will deliver the performance, reliability, and security that modern operations demand.

Curious how edge storage could strengthen your setup? Let’s chat about what fits your needs.

Useful Resources:

Cloud vs. edge – which is right for your business? Read out free ebook.

AI & Machine Learning

When Is It Time to Move from Cloud to Edge?

Jonny Cameron 19 June 2025

When you’re running critical workloads or rolling out the latest innovation, relying on the cloud can feel like second nature. After all, cloud environments offer scalable computing resources, flexible storage, and access to advanced cloud capabilities without needing massive data centers on-site.

Don’t get us wrong, we love the cloud and still see it as an important part of any infrastructure.

But there’s a point where the status quo starts holding you back.

Let’s look at when it makes sense to move from cloud to edge, and how edge deployments can unlock better performance, cost reduction, and resilience.

Frequent latency issues are slowing you down

Every millisecond counts when your systems need to process input data in real time. Think about autonomous vehicles, factory robotics, or telehealth systems, if network latency gets in the way, outcomes can suffer. Relying solely on cloud-based processing means sending data to the cloud and waiting for instructions. That works fine until distance and bandwidth bottlenecks create lag.

Edge computing refers to processing data closer to where it’s generated.

Edge devices, whether rugged edge servers in a plant or compact nodes at an IoT-heavy site, can handle decision-making on the spot.

Top Tip: If latency is a constant thorn in your side, consider deploying edge nodes strategically near your data source or end-users. You’ll cut out the lag and gain a speed boost that cloud systems alone can’t offer.

Data security and regulatory pressures are mounting

Sending sensitive data to centralized cloud servers means more points of exposure. Sure, top cloud providers invest heavily in security, but sometimes that’s not enough. With compliance changing fast, it may also not even be enough!

Industries bound by strict data localization rules, like finance or healthcare, often need to process and store data on-site.

Edge deployments offer a decentralized, efficient approach to data privacy. By keeping data processing at or near the source, you reduce the risk of interception during transmission. You also stay ahead of regulatory demands without having to navigate complex multi-region cloud configurations.

Top Tip: If your business model hinges on trust or compliance, say, in banking, healthcare, or government, edge solutions are worth exploring. Localized data processing is safer with far fewer compliance headaches.

IoT or AI workloads are overwhelming cloud reliance

IoT devices are everywhere now. From smart meters to connected medical equipment, these systems generate massive volumes of input data that can choke network resources if everything routes back to the cloud. The same goes for artificial intelligence and natural language processing workloads that need fast, local analysis to be effective.

Edge computing makes these technologies practical at scale. Instead of shipping everything off to massive data centers, edge devices handle immediate processing on-site. AI inferencing happens where the data lives. The result? Speed. Efficiency. Smarter operations.

The NUC 15 Pro Cyber Canyon, with AI-accelerated performance and Intel Arc graphics, is a compact option that packs a punch for local AI workloads without needing a giant server room.

Top Tip: If predictive maintenance, real-time analytics, or edge AI are part of your roadmap, it’s time to rethink where your processing happens. Edge can help you keep up with the pace of data generation.

You’re operating in challenging or remote locations

Some locations just aren’t cloud-friendly. Agriculture sites, kitchens with crazy temperatures, dusty locations, rural installations all present challenges that could stop your hardware in its tracks.

When reliable, high-speed connectivity isn’t guaranteed, cloud-based systems can fall short. Data might not make it to the cloud fast enough to be useful.

Edge computing provides autonomy. By deploying edge devices on-site, you can keep key applications running smoothly even when connectivity dips or drops altogether. Simply NUC’s extremeEDGE Servers™ are designed for this reality, supporting wide temperature ranges and harsh conditions without missing a beat.

Top Tip: If your operations span remote or connectivity-limited regions, edge computing can help keep data flowing, even without constant internet connectivity.

Cloud costs are spiraling

Cloud services offer scalability, but at a price. Between bandwidth fees, data transfer charges, and growing storage costs, your cloud bill can balloon as workloads scale. Sometimes, you’re paying to move data that could’ve been processed right where it was generated.

Edge deployments help balance the equation. By processing data at the source and sending only what’s necessary to the cloud, businesses reduce bandwidth use and cloud costs. It’s a smarter, more efficient approach that preserves cloud resources for what truly needs centralized scale.

Top Tip: Run a detailed audit of your cloud spend. If you’re moving massive amounts of data to the cloud only to process and discard it, edge computing could save serious money.

You need systems that don’t go down

Centralized systems introduce single points of failure. When cloud environments go offline, due to outages, cyberattacks, or even regional disasters, the ripple effects can cripple operations.

Edge computing offers a decentralized safety net. Edge nodes can keep critical systems running independently of the cloud, offering resilience that’s hard to match. Think of it as insurance for your infrastructure. Simply NUC’s edge-ready hardware can be part of that backbone, designed for reliability when it matters most.

Top Tip: If you’re in a sector where downtime is costly, transportation, utilities, emergency services, consider edge deployments as part of your resilience strategy.

Where to go from here

If you’re unsure about cloud vs edge, you should start by reading our free ebook.

Shifting from cloud to edge means blending the strengths of both to meet your evolving needs. Edge computing helps place computing resources closer to the data source for faster, smarter, and often cheaper processing. Cloud environments remain vital for scalable storage, analytics, and central management.

The right mix depends on your workloads, locations, and strategy. What’s clear is that edge isn’t just for tech giants or early adopters anymore. It’s a practical way to handle real-world challenges, from latency to cost reduction to security.

Curious how edge could fit your infrastructure? Let’s chat about what’s possible. Contact us here.

Useful Resources:

Cloud vs Edge – read our free ebook

AI & Machine Learning

How to Future-Proof Your Edge Computing Infrastructure

Jonny Cameron

Future-proofing your edge computing infrastructure is about making smart, lasting decisions that keep your systems flexible, efficient, and ready for whatever’s next.

As industries lean harder into AI, IoT, and automation, edge computing is fast becoming the backbone that keeps operations fast, secure, and resilient.

The right infrastructure can mean the difference between systems that evolve seamlessly and ones that hit a wall when new demands arise.

So, what does “future-proofing” really mean here?

It’s about building an edge computing setup that can grow, adapt, and thrive as technologies shift and workloads change. It’s about having hardware and architecture that won’t be obsolete the second new AI models or IoT devices hit the market.

It’s also about smart choices that reduce costs, improve security, and support real-time data processing where it matters most.

Choose modular, scalable hardware

The edge computing devices you deploy today need to be ready for what’s coming tomorrow. That’s where modular, scalable hardware comes in. Think of it as building with Lego blocks. You don’t want to tear down the whole structure when it’s time to upgrade, you want to swap out pieces and keep going.

Hardware to try:

Simply NUC’s extremeEDGE Servers™ are a great example. These rugged, industrial-grade units offer optional AI modules and flexible processor choices (AMD or Intel), so you can scale compute power or add AI inferencing without a full redesign.

Or take Onyx, with its PCIe x16 slot that lets you drop in a discrete GPU when your workloads start demanding more graphics muscle or AI acceleration. This kind of modular design means your edge computing architecture can flex as you add new services, support edge devices, or tackle bigger data processing challenges.

Prioritize rugged, industrial-grade design

Edge computing technology doesn’t always get to live in the comfort of a clean, climate-controlled office. Sometimes it’s out on a factory floor, in a remote energy site, or bolted into a moving vehicle. These environments hit your systems with dust, vibration, heat, cold, you name it.

That’s why rugged design is non-negotiable if you want edge computing infrastructure that stands the test of time.

Hardware to try:

The extremeEDGE Servers™ line is a good choice. These servers are fanless, industrial-grade, and built to handle wide temperature ranges. That means they keep working even when conditions get tough, supporting critical data processing for industries like manufacturing, energy, and transportation.

Enable AI at the edge

Edge computing and AI go hand in hand. Why? Because processing data locally, right where it’s generated, means faster decisions, lower latency, and reduced bandwidth costs. When you’re dealing with predictive maintenance on factory equipment or real-time video analytics on a smart city street corner, you can’t afford delays caused by shipping data off to a remote cloud data center.

Plan for remote manageability

One of the unsung heroes of future-proof edge infrastructure? Remote management. Your edge computing devices will often be out of sight, whether in a distant warehouse, along a transportation route, or on a wind turbine miles offshore. Getting boots on the ground to troubleshoot or update systems isn’t always practical, or affordable.

This is where features like a Baseboard Management Controller (BMC) become essential. Simply NUC’s extremeEDGE servers include BMC for out-of-band management, letting you monitor, update, and even repair systems without setting foot on-site. Their NANO-BMC technology adds an extra layer of flexibility for those compact deployments. Remote manageability means less downtime, lower maintenance costs, and a smoother experience scaling your edge network.

Think energy efficiency and form factor

Edge computing infrastructure needs to work hard and work smart. That means balancing performance with energy efficiency and space-saving design. Smaller, more efficient devices reduce operational costs, lower environmental impact, and fit into tight spots where traditional servers or data centers simply can’t go.

Simply NUC’s compact mini PCs and fanless options hit this sweet spot. They deliver the computing power edge services need, without the power-hungry overhead of larger systems. Whether you’re supporting edge computing in a smart city application, a retail kiosk, or a remote IoT node, these small-form-factor solutions make sure you’re not wasting watts, or rack space.

Future-proof with trusted partnerships and support

Here’s the thing, even the best edge computing hardware won’t take you far without the right partner backing you up. Future-proofing is about who you trust to stand behind that tech. That means looking for vendors who offer customization, testing, and solid support. Vendors who align their roadmaps with yours so you’re not caught off guard by the next big shift in edge computing technology.

Simply NUC delivers with their global support network, customization services, and commitment to helping businesses build edge computing solutions that last. Whether you need a micro modular data center setup or edge computing hardware fine-tuned for your environment, working with the right partner ensures you’re ready for whatever comes next

FAQ: Future-Proofing Edge Computing Infrastructure

What is edge computing infrastructure?

Edge computing infrastructure is the collection of edge computing devices, edge servers, edge data centers, and networking gear deployed at or near where data is generated. Unlike traditional cloud computing, which sends data to central data centers or remote data centers for processing, edge computing systems handle data closer to its source, right at the edge of the network. This setup significantly reduces latency, lowers bandwidth use, and improves privacy by keeping sensitive data local. Edge computing solutions are especially important for environments where real time data processing, predictive maintenance, or autonomous vehicles demand immediate action without waiting on cloud data centers.

What are examples of edge computing?

There’s no shortage of edge computing examples across industries. Think smart cities where sensors and cameras process data at the edge to manage traffic flow. Or manufacturing floors where edge computing enables businesses to perform predictive maintenance on smart equipment. Edge computing is also behind self-driving cars, helping them make split-second decisions based on data generated right on board. Even healthcare edge deployments use edge computing systems to process patient data locally, enhancing privacy and reducing the need to transmit data to centralized data centers. Basically, anywhere you need data processed closer to its source for speed, security, or bandwidth savings, that’s where edge computing shines.

What is the basic architecture of edge computing?

The architecture of edge computing combines local edge computing hardware, like edge servers, micro modular data centers, or rugged edge devices, with software and network services that manage computation and data storage right at or near the data source. This might involve edge data centers in a smart city, edge servers on a factory floor, or compact nodes embedded in smart devices.

Often, edge computing is combined with a fog computing layer that bridges the gap between edge deployments and cloud data centers. The goal? To process relevant data locally, store raw data or critical data as needed, and only transmit what’s necessary to the cloud, all while supporting edge devices and services efficiently.

What is the difference between cloud and edge computing?

The main difference between cloud and edge computing lies in where data is processed. Traditional cloud computing relies on centralized data centers or cloud providers' infrastructure to handle computation and data storage.

That works for many applications, but it can introduce latency, consume network bandwidth, and expose sensitive data in transit. Edge computing, on the other hand, processes data at the edge of the network, closer to where it’s generated. This edge strategy reduces reliance on cloud providers, cuts costs, and improves speed for real time data processing.

Fog computing and edge computing combined offer a middle layer between cloud and edge that helps manage data flow and computing power in complex edge computing environments. For businesses with smart devices, smart equipment data, or autonomous systems, edge computing offers clear benefits over traditional cloud setups.

Curious what that looks like for your setup? Let’s chat.

Useful Resources:

AI & Machine Learning

Which Edge Computing Hardware is Right for You?

Jonny Cameron

Choosing the right edge computing hardware is all about matching performance to your environment.

Are you crunching AI data on the edge of a factory floor? Or just need something reliable for daily business tasks in a tight space? Maybe you're deploying systems in a dusty warehouse or outdoors where fans won’t cut it, powering real-time video analytics for an AV setup in a busy conference center, or running edge devices in vehicles where vibration and temperature swings are constant. Perhaps you’re setting up hundreds of digital displays across retail locations and need something compact and easy to manage. Or maybe you just want the peace of mind that comes with full remote access, even when the device is powered off.

For heavy workloads and future-ready AI projects

Top Pick: NUC 15 Pro Cyber Canyon

With AI modeling, deep analytics, and demanding visual applications Cyber Canyon is your go-to. It’s built on Intel’s latest Core Ultra chips, with up to 99 TOPS of AI performance. Great for running multiple workloads or scaling up an edge AI deployment, this compact system handles it all without breaking a sweat.

Best for:

AI-powered edge inference
Real-time analytics
Multi-display digital signage
Performance-heavy industrial tasks

Why it works:

You get serious power in a footprint that still fits behind a screen or on a rack. It’s reliable, efficient, and won’t need upgrading anytime soon.

Find out more about NUC 15 Pro Cyber Canyon

For essential day-to-day edge tasks

Top Pick: NUC 14 Essential Mill Canyon

Sometimes you just need something simple that works. Mill Canyon is designed for everyday edge computing needs like signage, kiosks, and general business operations. It runs quietly, consumes very little power, and tucks easily into any setup.

Best for:

Point-of-sale and kiosks
Digital signage
Back-office systems
Light analytics or sensor hubs

Why it works:

It’s straightforward, energy-efficient, and affordable, perfect for rolling out at scale without the overhead cost.

Find out more about NUC 14 Essential Mill Canyon

For all-around flexibility across workspaces

Top Pick: Onyx

Onyx is an adaptable option for multiple scenarios. Whether you’re setting up an office deployment or integrating into a school or industrial space, this system offers balanced performance, optional vPro support, and flexible connectivity.

Best for:

General office or education environments
Multi-role IT deployments
Secure management (with optional vPro)
Training labs or smart classrooms

Why it works:
It’s reliable, versatile, and comes in different form factors to suit different needs—without overengineering the solution.

Find out more about Onyx

For teams that prefer AMD power

Top Pick: Moonstone

If AMD is your platform of choice, Moonstone delivers smooth multitasking with Ryzen processors. It’s a great fit for teams that rely on responsiveness, whether they’re managing data or working across multiple apps.

Best for:

AMD-based deployments
Visual and creative workflows
Mid-range business operations
Mixed OS environments

Why it works:
You get solid AMD performance in a compact form with modern connectivity and strong multitasking capabilities.

Find out more about Moonstone

For rugged environments and remote AI operations

Top Pick: extremeEDGE Servers™

Some edge environments aren’t friendly. For deployments in transportation, factories, energy, or remote monitoring, extremeEDGE Servers™ are purpose-built to handle it all. With fanless designs, wide temperature tolerance, and AI acceleration modules, these are designed to keep running no matter where you put them.

Best for:

Harsh outdoor or industrial environments
Remote operations or mobile units
AI inferencing at the edge
Distributed sensor hubs or gateways

Why it works:
With BMC-enabled remote access (even when powered off), you can manage fleets without sending

Find out more about extremeEDGE Servers™

FAQs

What kind of hardware do I need for edge computing?

It depends on your environment and workload. If you’re deploying IoT edge devices in a harsh location, you’ll want rugged, compact devices with strong processing power, like our extremeEDGE Servers™. For everyday business tasks or inventory management, a fanless unit like Mill Canyon does the job with minimal fuss. Edge computing hardware refers to the physical components; edge computers, edge routers, and storage, that enable localized data processing.

How does edge computing compare to cloud computing?

Cloud computing stores and processes data in large, centralized data centers. It’s great for backups and scalable storage. But edge computing processes data locally, at the network edge, making it faster and often more secure. In many cases, a hybrid cloud setup works best, combining edge performance with cloud scale.

Can edge computing work in remote or low-connectivity areas?

Yes. That’s one of its biggest advantages. Edge solutions like Simply NUC’s ruggedized systems are designed to operate reliably even when network connectivity drops. They continue to process critical data on-site, making them ideal for remote locations like oil fields, utility stations, or mobile fleet operations.

What industries benefit most from edge computing?

Industries that generate large volumes of operational or visual data, like manufacturing, healthcare, retail, transportation, and energy, see big wins with edge computing. From enabling predictive maintenance on machines to analyzing video footage for safety compliance, edge technology is helping businesses move faster, reduce downtime, and improve service delivery.

How does edge computing help with real-time data processing?

By processing data on the spot, edge computing reduces latency and enables split-second decisions. This is especially important for machine vision, autonomous systems, and time-sensitive tasks like equipment monitoring or public safety responses. Edge computing devices don’t need to wait for the cloud to respond, they handle it locally.

What features should I look for in edge computing devices?

Look for compact devices that balance performance and durability, like Onyx or Cyber Canyon. Key features include solid-state drives for faster data access, wireless connectivity options like Wi-Fi or 5G, energy efficiency, and compatibility with common operating systems. You might also need remote management tools, especially for managing devices across multiple edge locations.

Is edge computing secure?

Yes, especially because it processes data closer to the source. This limits how much sensitive data is transmitted across networks, reducing the chance of breaches. Many Simply NUC systems also offer secure boot, TPM modules, and remote management tools that help maintain tight control over your computing hardware.

Can edge computing support AI or machine learning?

Absolutely. Edge nodes equipped with the right compute resources can run AI models locally, enabling things like real-time image recognition or anomaly detection. Our Cyber Canyon and extremeEDGE solutions are built with this in mind, supporting high-performance workloads where data needs to be processed quickly and locally.

How do I know which edge solution is right for my business unit?

Start with your environment. Are you working in an AV installation, a smart warehouse, or a retail floor? Then consider the data volumes, the need for real-time processing, and your power/network limitations. From there, we can help you choose a device, from basic, energy-efficient setups to powerful systems with full AI acceleration.

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Cloud vs edge computing – which model is rigth for your business?

AI & Machine Learning

Smooth Scalability With Edge Computing

Jonny Cameron

When growth is on the horizon, leveraging edge computing helps businesses move faster, stay agile, and scale with less friction. By shifting computing power closer to where data is generated, it removes common roadblocks to expansion and opens the door to real-time intelligence at the edge. Here’s how it supports growth at every stage:

1. Faster services, better experiences
Speed matters when scaling. Edge computing cuts out the delay of sending data back and forth to a central cloud, making everything from smart sensors to industrial automation work in real time. That responsiveness helps businesses deliver better products, services, and customer experiences, without lag.

2. Lower costs as you grow
Scaling doesn’t have to mean spiraling cloud costs. With edge infrastructure, businesses can reduce bandwidth demands by processing data locally. Only the most essential data is sent to the cloud, helping control costs while still gaining valuable insights.

3. Scale operations without overhauls
Need to expand into new regions or add capacity for more devices? Edge computing makes it easy to roll out new resources locally, without reworking core systems. That modular flexibility is perfect for growing businesses that want to move fast without massive IT projects.

4. Stay online, even when the cloud isn’t
Downtime can derail growth. With edge systems running independently of central servers, critical operations can keep going even if cloud access is interrupted. That reliability is key for sectors like healthcare, manufacturing, or retail, where every second counts.

5. Ready for the IoT boom
More devices mean more data. Edge computing handles that increase by analyzing data close to its source, enabling fast decisions and real-time insights. This makes it easier to scale your IoT ecosystem without overwhelming your network or cloud storage.

6. Grow with confidence in compliance
Scaling often means operating in new markets with different rules. Edge computing supports local data processing and storage, which can help meet data sovereignty and compliance requirements more easily, especially when handling sensitive customer information.

7. Personalization at scale
Want to offer tailored experiences as you grow? Edge devices can analyze behavior on the spot, helping businesses personalize services in real time, whether that’s in a retail store or a smart kiosk. The result is better engagement and higher customer satisfaction.

8. Experiment without limits
Edge computing supports rapid innovation. Businesses can test new ideas, deploy updates locally, and explore emerging technologies without placing strain on central systems. That freedom to experiment fuels long-term growth and competitive advantage.

Edge computing vs traditional models

Let’s break down how edge computing compares to the more traditional approach. In a typical setup, everything runs through central data centers. That means all the data from devices has to travel all the way to a remote server just to be processed. When the volume ramps up, this model can slow things down and stretch bandwidth to its limits.

Edge computing takes a different route. Instead of pushing everything to the cloud, it processes data right where it’s created. That local approach reduces delays, frees up bandwidth, and makes systems more responsive. It’s like having decision-making power built into each device instead of sending every request to HQ.

At Simply NUC, we’ve designed our edge servers to work exactly where the action happens, even in extreme conditions. That means businesses can sidestep slowdowns, manage data more efficiently, and keep things running smoothly without relying too heavily on centralized infrastructure.

Here’s the real advantage: with this distributed model, businesses can avoid bottlenecks, as well as opening the door to new opportunities. Whether it’s running real-time analytics at the edge or keeping sensitive data local for better security, edge computing gives growing organizations more control, more speed, and more room to innovate.

How edge computing supports growth

Here’s the thing about scaling a business, everything seems to speed up. More users, more devices, more data. Traditional systems start to strain under the pressure, and delays can creep in just when performance matters most.

Edge computing changes the game by processing data closer to the point of action, which is essential when your operations rely on real-time results. For example, if you’re tracking equipment in a warehouse or serving personalized content in-store, you get the speed and precision needed to keep things flowing.

Even better, edge computing grows with you. We like to think of it as limitless.

You can add new edge nodes wherever they’re needed with no need to rip up and rebuild your core infrastructure. That flexibility means businesses can expand operations without sacrificing performance or uptime.

Because edge systems reduce latency, your team gets the insights they need instantly. That’s especially important when you're deploying AI or automation tools. It allows you to react quickly and make smarter decisions, faster.

Real-world business scenarios where edge supports scale

Smart stores are using edge devices to handle everything from real-time inventory tracking to automated checkout. Because the data is processed on-site instead of being sent off to the cloud, stores can scale operations faster without overloading their IT systems.

This helps retailers with shorter queues, smarter stock management, and a better overall experience for customers.

In manufacturing, edge computing helps IoT sensors on machines gather data like temperature and vibration, then analyze it locally to spot problems before they cause downtime. This kind of predictive maintenance helps factories expand across multiple sites without losing efficiency or sleep over unexpected breakdowns.

Healthcare providers are also getting a major boost as edge computing allows clinics, even in remote areas, to run real-time diagnostics and monitor patient vitals locally. It means doctors and nurses don’t have to wait for cloud servers to deliver results. They can act fast and give better care where and when it’s needed most.

In logistics, edge technology is helping fleet managers make smarter calls. Whether it's rerouting delivery vans or keeping autonomous vehicles on track, having compute power right on board means decisions get made instantly, even in areas with poor connectivity. That speed and flexibility is a big win for any company scaling operations across new territories.

In public transportation, edge computing is helping big cities to modernize fleet operations. Rugged fanless systems can withstand the constant vibration and power fluctuations on buses. Edge devices support real-time data processing for onboard cameras and systems, even when the ignition is off. With features like remote management and programmable DC boost control, transport providers are able to scale across both electric and diesel fleets, improving safety, reducing downtime, and ensuring a consistent passenger experience citywide.

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AI & Machine Learning

Steps to Create The Perfect Edge Computing Architecture

Jonny Cameron

You already know that edge computing brings data processing closer to where it’s needed, cutting latency, boosting performance, and enabling real-time decisions.

But how do you actually build an IT infrastructure that delivers on that promise?

If you're planning to deploy or scale an edge solution, getting the architecture right is key. In this guide, we’ll walk through eight practical steps to help you design an edge architecture that’s reliable, efficient, and ready to grow with your business.

Why architecture matters at the edge

Edge architecture is the foundation that keeps data moving, decisions happening in real time, and operations running smoothly, even in tough environments or remote locations. From a sensor on a truck to a smart camera in a store, the whole system depends on having the right infrastructure behind it.

Here, we’ll walk you through how to design edge architecture that’s built to last, how to pick the right hardware, and what makes a deployment successful, whether you’re just getting started or scaling fast.

Get your edge architecture wrong, and you risk bottlenecks, downtime, and costly inefficiencies that can slow your entire operation.

What goes into your edge architecture?

Think of your edge setup like a well-organized relay team, every part has a role, and the timing has to be spot on.

It all starts with your edge devices. These are the data collectors: sensors, cameras, and other smart endpoints. From there, edge servers take the baton, processing that data locally so insights can be acted on immediately. Depending on your setup, some data might head to the cloud for storage or deeper analysis, but not everything needs to make that trip.

To keep things fast and efficient, many setups use local or micro data centers. These are small but mighty hubs that help handle the load without involving a distant central server. That means faster responses, better resilience, and a lighter load on your network.

Choosing the right mix of devices, designing how they connect, and making sure everything fits your environment is vital. Done right, your edge architecture will be ready for almost anything.

Hardware and resource planning

Let’s get into the practical side of edge computing, what kind of gear do you actually need?

Start by looking at the workload. Are you running lightweight monitoring software or heavy-duty AI inference models? That will determine your compute needs: CPU power, memory, storage, and maybe even dedicated GPUs or AI accelerators.

Next, think about where it’s going. A controlled indoor environment is one thing. A dusty warehouse or roadside cabinet is another. That’s why rugged design, fanless cooling, and compact form factors matter so much. The device has to perform reliably without needing constant attention.

We offer custom-configured systems at Simply NUC, so you get exactly what you need to support your IT infrastructure.

Your checklist

Here’s a breakdown of 8 essential steps to consider when creating your edge architecture:

Define the business goals and use case
Understand the problem you're solving. Are you enabling real-time analytics? Reducing latency in manufacturing? Supporting IoT in retail? Powering AV installations in remote locations?
Map data sources and edge locations
Identify where your data is being generated; IoT devices, sensors, cameras, and determine which locations need local processing versus central aggregation.
Select the right edge hardware
Choose edge devices that match the workload. This includes ruggedized mini PCs or edge servers (like Simply NUC’s extremeEDGE™ servers) with the right CPU/GPU, memory, and form factor for the environment.
Choose your software stack and OS
Pick an operating system and runtime environment suited to your application (e.g., Linux for industrial, Windows for office environments), and make sure it supports containerization or orchestration if needed.
Design for connectivity and networking
Determine how edge devices will communicate with each other, the cloud, and back-end systems. Plan for intermittent connectivity, low bandwidth, and secure data transmission.
Plan for data management and storage
Decide what data gets processed locally, stored temporarily, or forwarded to the cloud. This includes filtering, compressing, and securing the data at the edge.
Build in security and compliance from the start
Consider encryption, secure boot, identity management, and data sovereignty. This is especially critical in sectors like healthcare, finance, and defense.
Enable remote management and scalability
Use tools like BMC (Baseboard Management Controller) or Simply NUC’s NANO-BMC for remote monitoring, updates, and troubleshooting, essential when deploying at scale or in hard-to-reach places.

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AI & Machine Learning

Revolution at the Edge: How Embedded Systems Are Shaping the Future of Computing

Jonny Cameron

From smart kiosks to autonomous vehicles, these compact, efficient machines are redefining what edge computing can do.

We’ve seen firsthand how embedded edge systems help businesses stay agile and responsive in the face of rising data demands. This article dives into how embedded systems are changing the game, where they fit in, and why they’re shaping the future of computing, one real-time decision at a time.

What are embedded edge systems?

Embedded edge systems are purpose-built computing devices designed to handle specific tasks right where data is created. They combine the power of embedded computing with the responsiveness of edge infrastructure, meaning they can process, analyze, and act on data locally, without needing to send everything back to the cloud.

These systems are often compact, rugged, and energy-efficient, making them ideal for environments where space is tight, conditions are tough, or constant connectivity isn’t guaranteed. Think of digital signage that updates in real time, or factory equipment that flags a fault before it breaks down.

At Simply NUC, we provide hardware that supports embedded edge solutions to meet real-world demands, with long-life reliability, customizable configurations, and remote manageability.

extremeEDGE Servers™ EE-1000 / EE-2000 / EE-3000
Rugged, fanless servers designed for harsh environments, with BMC-enabled remote management and PCIe expansion for AI modules or additional storage.

NUC 15 Pro Cyber Canyon
Compact mini PC with enterprise-level performance, supporting upgradeable storage and memory, ideal for edge deployments that need flexibility in a small form factor.

Onyx
Powerful mini PC featuring high-end CPU and GPU options, PCIe and M.2 expansion slots, and optional AI acceleration for demanding edge workloads like analytics or machine vision.

Why embedded edge systems matter for modern computing

Modern computing isn’t confined to data centers anymore. Businesses need speed, reliability, and local decision-making, especially when real-time performance makes a difference.

These systems matter because they bring computing closer to the action. Whether it's monitoring equipment on a manufacturing line, managing smart energy systems, or enabling AI vision in retail, embedded edge solutions process data on-site, in real time. That cuts out latency, boosts reliability, and helps keep sensitive data secure.

Simply NUC customers are already seeing the difference. With edge-ready hardware that’s small enough to fit just about anywhere, but powerful enough to handle AI, analytics, and control systems. Our small form factor devices help deliver faster insights and smarter automation without needing to rely on constant cloud access. With remote management through tools like NANO-BMC, your IT team stays in control, even if the device is miles away.

Real-world impact: Embedded edge in action

If you work in manufacturing: Instead of sending every bit of sensor data to the cloud, edge systems can analyze performance right on the factory floor. That means spotting a faulty part before it causes a shutdown. It's faster, more efficient, and helps avoid costly delays.

In retail, embedded edge devices are driving smarter in-store experiences. Think digital signage that adapts in real-time based on foot traffic or temperature sensors that adjust cooling systems automatically. It’s all happening quietly in the background, making spaces more responsive without relying on a distant data center.

Healthcare, too, is being transformed. Edge systems embedded into diagnostic tools and monitoring equipment help deliver faster results, improve patient care, and keep sensitive data on-site.

What makes embedded systems at the edge unique

Embedded edge systems are tailored for very specific tasks, often in very specific places. Whether it’s controlling a robot arm on a production line or analyzing sensor data in a smart thermostat, their magic lies in doing one job well, right where the data is being created.

Here’s what sets them apart:

Localized processing: Instead of sending everything to the cloud, embedded systems handle data on the spot. That means decisions happen fast, which is critical when timing is everything, like in a hospital or on a busy highway.
Compact, efficient design: These systems are small, tough, and built for purpose. You’ll find them embedded in machinery, walls, dashboards, basically anywhere that needs computing muscle without the bulky footprint.
Seamless with IoT: They’re the glue behind the Internet of Things. From factory sensors to home automation, embedded edge systems are what make “smart” devices truly smart.
Built to conserve power: Because they’re often working in remote or power-sensitive locations, these systems sip energy. That makes them a good fit for long-term, low-maintenance deployments.
Real-time ready: Many embedded systems are designed for split-second reactions, for example, monitoring a patient’s heart rate or detecting an obstacle in front of a driverless car.
Always connected: While they work independently, these systems still play nice with others. With built-in Wi-Fi, Bluetooth, or 5G, they’re ready to sync with the cloud or communicate with nearby devices when needed.

The real-world benefits for business

So what does all this mean when you’re scaling operations, improving customer experiences, or streamlining processes?

Lightning-fast responses: With processing handled locally, latency drops dramatically. That’s a game-changer for time-critical environments like emergency healthcare or smart traffic systems.
Rock-solid reliability: If the internet goes down, your edge systems won’t. These devices are built to keep running, even when connectivity doesn’t cooperate.
Stronger security: Data stays closer to its source, reducing the chance of it being intercepted in transit. For industries handling sensitive info, finance, health, government, that’s a major win.
Lower operating costs: By cutting down the volume of data sent to the cloud, you’re also cutting cloud storage and bandwidth bills. That adds up quickly across multiple sites or devices.

Where embedded edge systems are already making an impact

You might not always see them, but embedded edge systems are working behind the scenes in all kinds of industries:

Smart cameras use onboard AI to analyze video footage as it’s captured, ideal for facial recognition in retail or traffic monitoring in smart cities.
Industrial IoT gateways keep a finger on the pulse of factory equipment, enabling real-time diagnostics and predictive maintenance.
Autonomous vehicles rely on embedded systems to process massive amounts of sensor data locally, think cameras, radar, and LiDAR, all in milliseconds.
Wearable health devices track vitals in real time, flag anomalies, and only send summaries to the cloud when needed. It’s smarter and more private.
Smart home devices like thermostats and security systems use local processing to respond instantly, keeping your environment comfortable and secure without delay.

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