Edge

Edge computing in financial services

Understanding edge computing and sustainability

As technology continues to scale, so does its environmental footprint. The good news is, using edge computing solutions offers a more efficient way to handle data, one that’s better for business and better for the planet.

Instead of sending information across long distances to centralized cloud servers, edge computing processes data closer to where it’s created. This localized approach doesn’t just speed things up; it also reduces energy use and helps lower the environmental impact of digital operations.

Why edge computing supports greener tech

Sustainability in tech is about rethinking how systems are designed. By moving away from massive, energy-intensive data centers, edge computing helps organizations meet sustainability targets while improving performance.

Here are a few ways edge computing supports more responsible infrastructure:

Improved energy efficiency
Local data processing cuts down on the energy needed to transmit information across long networks.
Less reliance on large data centers
With more tasks handled at the edge, there’s less pressure on central servers that consume huge amounts of power.
Lower latency, higher efficiency
When systems respond faster, they work smarter. That means less energy wasted in waiting, rerouting, or reprocessing.
Better use of IoT resources
Devices that process data locally can make smarter decisions in real time, which helps reduce overall energy use.

These shifts might seem small in isolation, but together, they can significantly reduce the carbon footprint of everyday digital activity.

The environmental cost of centralized cloud models

Traditional cloud infrastructure leans heavily on centralized data centers. These facilities require a lot of power to run and even more to cool. That setup creates several challenges:

High power usage
Data centers demand large-scale electricity just to stay online, even when handling routine tasks.
Excessive heat output
Servers generate heat that must be constantly managed through cooling systems, which adds to total energy consumption.
Increased carbon emissions
Sending data long distances over global networks burns energy and contributes to higher carbon output.

When you compare this to edge computing, where processing happens closer to the user, it’s easy to see the sustainability benefits. Fewer trips to the cloud means less energy spent and more efficient use of hardware on the ground.

A more efficient path forward

Edge computing technology is helping organizations process data where it’s generated, instead of relying on centralized servers or distant cloud data centers.

This approach reduces energy usage, shortens the path for data transmission, and allows for faster response times.

By deploying edge devices across local networks, businesses can cut down on unnecessary cloud traffic, reduce electricity consumption, and ease the load on data storage systems. The shift toward real time data processing doesn’t just improve network speed or operational efficiency; it also supports sustainability strategies by reducing power consumption and limiting reliance on more data centers.

Whether it's powering smart buildings, enabling responsive IoT networks, or streamlining enterprise data workflows, edge computing solutions are helping businesses move toward more sustainable practices without sacrificing performance.

Use cases that show the sustainability benefits of edge computing

Edge computing's role in sustainability goes well beyond speed. It’s playing a part in how industries rethink infrastructure, minimizing greenhouse gas emissions, reducing electronic waste, and improving resource efficiency.

Let’s look at how edge servers are enabling businesses across different sectors to reduce energy consumption and move toward a more sustainable future.

Smarter energy management in modern grids

Real time data processing is key to maintaining balance and reliability. Edge computing devices installed across the grid allow for instant monitoring and adjustments based on demand.

Data from sensors is processed at the edge, which reduces the need to send all this data to cloud computing platforms. As a result, these systems require less computing power, reduce power consumption, and optimize how energy flows from renewable energy sources like solar and wind.

The outcome is clear: less energy waste, improved electricity distribution, and more efficient operations.

Supporting sustainable cities

Urban environments generate massive amounts of data, traffic flows, public transport schedules, air quality readings, and more. Edge computing stores and processes that data locally, making it easier for systems to respond in real time.

Edge-powered platforms support smart city applications like AI-powered traffic signals and dynamic waste collection routes. By handling data closer to the source, cities reduce network traffic, improve decision-making, and reduce their reliance on centralized cloud data storage. That translates into reduced energy requirements and better support for long-term sustainability strategies.

Energy-efficient smart homes and smart buildings

IoT-enabled devices are everywhere, from thermostats and lighting systems to smart plugs and HVAC units. With edge computing, these electronic devices don’t have to rely on cloud data centers for every function. Instead, they make localized decisions using built-in computing power.

This shift results in lower data transmission needs and meaningful energy savings for consumers.

It also helps manufacturers position energy-efficient edge computing devices as part of a greener technology stack, appealing to homeowners who want to reduce energy usage and support more sustainable operations.

Lower-impact healthcare systems

Healthcare is generating more data than ever. From remote patient monitoring to advanced machine learning diagnostics, real time analysis is critical, but relying on cloud computing alone adds strain to centralized infrastructure.

Edge computing allows wearable medical devices and monitoring tools to process patient data on-site. This helps reduce reliance on backend systems, minimizes electricity consumption, and lowers the environmental impact tied to powering and cooling cloud infrastructure.

Telemedicine systems benefit too. Edge computing keeps services online and responsive without relying solely on large-scale data centers, improving both efficiency and sustainability across the healthcare technology stack.

Challenges and solutions in building sustainable edge systems

While edge computing has clear benefits for sustainability, it isn’t without its hurdles. Like any shift in technology infrastructure, the transition to a more energy-efficient model comes with trade-offs that need thoughtful planning.

Here’s a closer look at the common challenges and how organizations are working through them.

Challenges to consider

Initial energy demand

Rolling out edge devices at scale often increases total hardware usage. That means energy consumption can rise at the beginning of a deployment, even if it lowers over time.

Renewable integration isn’t automatic

Bringing clean energy into edge infrastructure isn’t always straightforward. Powering local systems with renewables depends on access, geography, and planning, and those pieces don’t always align out of the box.

Harder-to-monitor infrastructure

Edge systems are spread out, which makes it more difficult to track and optimize energy performance. Without proper tools, maintaining sustainable practices across multiple locations can be a challenge.

Practical solutions that make a difference

Low-power edge devices

Choosing energy-efficient hardware helps reduce the impact of large-scale rollouts. Smaller devices with optimized power usage can provide the performance needed without unnecessary draw.

Smarter management platforms

Monitoring platforms designed for distributed systems can give teams real-time visibility into energy use, performance, and uptime. This kind of insight helps ensure systems run as efficiently as possible.

Working with renewable energy providers

Partnering with green energy suppliers, or building edge infrastructure near renewable sources, can help ensure systems run on clean power. It’s an extra step that adds long-term value for both sustainability and resilience.

By facing these challenges head-on and applying the right tools, organizations can keep their sustainability efforts on track while still taking advantage of the performance benefits edge computing provides.

How edge computing helps reduce carbon over time

The long-term sustainability of edge computing lies in its ability to do more with less; less distance, less energy, and less reliance on centralized infrastructure. By processing data where it’s created, edge systems reduce the need to push everything back to the cloud. That leads to more efficient energy use and a lower overall footprint.

As businesses explore more decentralized energy models and adopt green initiatives, edge computing fits naturally into the strategy. Here’s how:

Smaller, cleaner energy footprints
Local systems make it easier to run on solar, wind, or other renewable sources, reducing dependency on traditional grids.
More sustainable digital infrastructure
With less pressure on data centers and a shift to smarter local processing, edge computing makes it easier for businesses to operate sustainably.
Support for global emission goals
By reducing redundant cloud traffic and unnecessary energy use, edge computing plays a role in helping industries lower their carbon output.

Looking ahead, the environmental impact of digital systems will only become more important. Edge computing gives businesses the tools to build for performance today, while helping protect the environment for tomorrow.

*Sustainability survey

AI & Machine Learning

Myth-Busting: Edge AI Is Too Complex to Set Up

Jonny Cameron 15 May 2025

Just when you get used to the idea of AI, along comes “Edge AI”.

At first it conjures images of servers in remote locations, machine learning models, industrial systems, and maybe even a few sci-fi undertones. It sounds like something that requires a team of engineers and a mountain of infrastructure just to get started.

But that’s the myth. And it’s time we cleared it up.

The truth? Edge AI has come a long way in a short space of time and setting it up is more approachable than most people think.

Why this myth exists in the first place

A few years ago, getting AI to run at the edge wasn’t easy. You had to pull together custom-built hardware, optimize machine learning models by hand, and write scripts just to get devices talking to each other. It worked, but only for the teams with deep technical know-how and plenty of resources.

Because “AI” and “edge computing” are both complex topics on their own, combining them sounds like it would double the effort. Spoiler: it doesn’t anymore.

Edge AI setup isn’t what it used to be (in a good way)

Today, it’s a different world. The tools have matured, the hardware has gotten smarter, and the whole process is a lot more plug-and-play than people expect.

Here’s what’s changed:

Hardware is ready to roll
Devices like Simply NUC’s extremeEDGE Servers™ come rugged, compact, and purpose-built to handle edge workloads out of the box. No data center needed.
Software got lighter and easier
Frameworks like TensorFlow Lite, ONNX, and NVIDIA’s Jetson platform mean you can take pre-trained models and deploy them without rewriting everything from scratch.
You can start small
Want to run object detection on a camera feed? Or do real-time monitoring on a piece of equipment? You don’t need a full AI team or six months of setup. You just need the right tools, and a clear use case.

Real-world examples that don’t require a PhD

Edge AI is already working behind the scenes in more places than you might expect. Here’s what simple deployment looks like:

A warehouse installs AI-powered cameras to count inventory in real time.
A retail store uses computer vision to track product placement and foot traffic.
A hospital runs anomaly detection locally to spot equipment faults early.
A transit hub uses license plate recognition—on-site, with no cloud lag.

All of these can be deployed on compact systems using pre-trained models and off-the-shelf hardware. No data center. No endless configuration.

The support is there, too

Here’s the other part that makes this easier: you don’t have to do it alone.

When you work with a partner like Simply NUC, you get more than just a box. You get hardware tuned to your use case, documentation to walk you through setup, and support when you need it. You can even manage devices remotely using side-band management, so once your systems are up and running, they stay that way.

We’ve helped teams deploy edge AI in manufacturing, healthcare, logistics, retail, you name it. We’ve seen firsthand how small, agile setups can make a huge difference.

Edge AI doesn’t have to be hard

So here’s the bottom line: Edge AI isn’t just for tech giants or AI labs anymore. It’s for real-world businesses solving real problems – faster, smarter, and closer to where the data lives.

Yes, it’s powerful. But that doesn’t mean it has to be complicated.

If you’re curious about how edge AI could fit into your setup, we’re happy to show you. No jargon, no overwhelm, just clear steps and the right-sized solution for the job.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

Blog

Top 5 Intelligent Edge Devices Transforming IoT

Jonny Cameron

As connected systems continue to scale, the Internet of Things (IoT) is generating more data than ever before. From smart factories to healthcare monitoring, everything relies on fast, reliable insights.

That’s where intelligent edge devices come in.

Instead of constantly sending data to a central server for processing, these devices handle much of the work locally.

They collect data, process it in real time, and only transmit what’s necessary to the cloud. The result? Faster decisions, reduced data traffic, and improved performance across the board.

In this blog, we’re exploring five standout edge computing devices that are helping redefine how IoT solutions operate. From compact, small form-factor, like Simply NUC systems to AI-ready tools such as Nvidia Jetson Nano, these edge devices are shaping a smarter, more responsive future.

We'll also take a look at the features that make certain devices more transformative than others, and how intelligent edge computing is streamlining operations across industries like industrial automation, agriculture, and smart cities.

What makes intelligent edge devices different?

Not all edge computing devices are built the same. You may think edge devices typically act as a pass-through, collecting sensor data and forwarding it to cloud services. But intelligent edge devices do more.

They process data locally, using embedded AI and machine learning models to analyze, filter, and act on that data in real time. Whether it’s reducing downtime through predictive maintenance or managing data traffic between multiple sensors, the intelligent edge gives businesses the power to adapt and respond faster than ever.

By limiting the need to transfer data constantly to a cloud service or central server, these devices reduce latency, ease network congestion, and support greater operational efficiency. They also help manage data flow more intelligently by storing relevant information locally while sending only critical data to the cloud for further analysis.

In Azure-based environments, for example, the Azure IoT Edge runtime allows businesses to run Azure services, machine learning models, and existing business logic right on the edge. This means you can deploy Azure Stream Analytics, Azure Machine Learning, and other Azure IoT services on local devices, avoiding delays tied to cloud-only architectures.

Top 5 intelligent edge devices transforming IoT

As IoT devices continue to evolve, so does the need for powerful, flexible tools that can analyze data, make decisions, and operate reliably at the edge. The devices listed below show how modern edge computing platforms are helping businesses collect data, process it in real time, and maintain secure, resilient operations across a wide range of environments.

1. Simply NUC

Simply NUC delivers compact, configurable, and high-performance systems designed to meet the growing needs of edge computing. These small-form-factor edge computing devices are particularly well suited for industrial automation, healthcare diagnostics, and smart retail applications where real time data analysis is essential.

Simply NUC systems support Azure IoT Edge deployments and can run AI modules, predictive maintenance models, and other business logic directly at the edge. With local data processing and compatibility across multiple networks and protocols, they enable secure, scalable solutions that reduce latency, increase uptime, and minimize unnecessary data transmission to the cloud.

Supports local data processing with minimal latency
Ideal for analyzing data from multiple sensors in real time
Easily integrates with Azure IoT Edge runtime
Customizable configurations to fit specific workloads and equipment failures scenarios
Designed for network connection reliability in industrial and remote environments

2. Nvidia Jetson Nano

The Nvidia Jetson Nano is a compact, AI-ready platform that brings serious computing power to the edge. It’s widely used in applications such as robotics, smart surveillance, and smart city deployments.

With onboard support for machine learning, the Jetson Nano allows developers to run deep learning models on-device. This makes it possible to analyze data from cameras and sensors in real time, avoiding the delay and cost of sending everything to a central server.

Its ability to transmit data only when needed supports better bandwidth management and power efficiency, especially in edge environments where every watt matters.

Delivers AI and computer vision capabilities on a small board
Supports data storage and inference at the data source
Integrates well with cloud computing platforms for hybrid processing
Ideal for environments with intermittent network connection

3. Raspberry Pi 4 with AI modules

For developers and small teams, the Raspberry Pi 4 offers an affordable and accessible way to experiment with edge intelligence. When equipped with AI modules, it becomes a capable IoT edge device for prototyping and small-scale deployments.

It can run simple AI tasks like image classification or voice recognition while storing data locally and responding quickly to input from connected IoT devices. It also supports IoT Edge runtime compatibility for running lightweight services offline.

This makes the Pi 4 ideal for projects that require real-time action, tight data security, or quick deployment without the overhead of cloud services.

Great for testing AI modules in local environments
Supports local data storage and real-time data analysis
Works well with other devices in custom IoT setups
Reduces reliance on centralized cloud computing

4. AWS Snowcone

AWS Snowcone is built for rugged environments where space, power, and connectivity are limited. It combines edge computing and cloud storage in a portable form factor that can operate independently or connect with AWS cloud services when available.

Snowcone is often used for equipment monitoring in remote or offline locations. It allows businesses to store data locally, process it at the edge, and later transmit data back to the cloud for further analysis once a stable connection is restored.

For businesses operating across disconnected networks or in mobile settings, Snowcone offers a practical way to maintain operational continuity.

Portable and durable for challenging edge environments
Connects to IoT hub and other AWS services
Manages data flow between local and cloud systems
Designed to prevent data loss during network connection drops

5. Intel Movidius Neural Compute Stick

The Intel Movidius Neural Compute Stick is a plug-and-play AI accelerator that allows developers to run machine learning models directly on edge devices. Despite its small size, it delivers powerful capabilities for real time data analysis—particularly in use cases like smart home automation, robotics, and security systems.

By processing data at the edge network instead of routing it to a central server, the Compute Stick enables low-latency performance, enhanced privacy, and reduced energy use.

It’s a lightweight yet capable tool for integrating intelligence into physical devices without the need for constant cloud connectivity.

USB form factor makes it easy to integrate into existing infrastructure
Optimized for AI workloads including vision and language
Supports fast local inference to avoid data transmission delays
Useful in projects where sensitive data should stay on-site

Why these edge devices matter

Each of these edge computing platforms demonstrates a shift in how we manage and use data across connected systems. They show how the intelligent edge is transforming traditional cloud models into more distributed, responsive, and secure infrastructures.

By enabling real-time insight, minimizing bandwidth, and supporting decentralized data storage, these devices are helping businesses reduce complexity and increase control.

Advantages of intelligent edge devices in IoT

Choosing the right edge device is all about enabling your business to act faster, scale smarter, and operate more securely.

Here’s what intelligent edge computing brings to the table:

Lower latency
Local data processing allows systems to act instantly, which is vital for time-sensitive environments like autonomous vehicles, smart cities, or safety systems.
Stronger reliability
Fewer dependencies on cloud access mean systems keep running, even when internet connectivity is spotty or unavailable.
Better energy efficiency
By minimizing data transmission and reducing power-hungry cloud interactions, edge devices help lower operational energy requirements.
Improved security
With data stored locally and processed on-site, there’s less exposure to outside threats, especially in industries that handle sensitive data.
Cost savings
Less reliance on cloud infrastructure cuts recurring costs related to bandwidth, data storage, and server usage.

These advantages make intelligent edge devices a compelling choice for any organization looking to boost performance while building more resilient and sustainable IoT systems.

What to look for in a next-generation edge device

The ideal device should offer the speed, flexibility, and intelligence needed to manage data, run AI models, and operate smoothly across a range of environments.

Here’s what to keep in mind when evaluating your next Azure IoT Edge device or edge-ready platform:

Performance at the edge

To make local processing work, edge devices must handle large volumes of sensor input while delivering real time insights. Look for systems that support artificial intelligence and machine learning especially if your workloads involve smart factories, robotics, or high-speed logistics. The best devices can run AI modules, detect patterns, and make decisions in milliseconds.

Strong and stable connectivity

For any edge device to work well, it needs a reliable connection to your local network, IoT sensors, and cloud infrastructure. Support for Wi-Fi, 5G, and lightweight protocols like MQTT ensures devices can transmit data and stay synced with other systems, no matter the location.

Energy-efficient design

Edge devices often run in power-constrained or remote locations. Low-energy designs help keep systems online longer while reducing heat, noise, and environmental impact. If you're deploying edge devices in smart buildings or agricultural fields, power efficiency directly supports sustainability and cost savings.

Built-in scalability

Whether you're rolling out in five locations or fifty, your edge devices should scale with ease. Devices that support Azure IoT Edge make it easier to roll out updates, manage security, and integrate with existing data management platforms. They should also work with multiple smart devices, including sensors, gateways, and local controllers.

Security services by design

When more devices operate at the network edge, strong security becomes essential. Look for edge systems that include features like secure boot, data encryption, and integration with Microsoft Azure security services. These protections help guard against cyber threats while ensuring compliance for sectors like healthcare, finance, and retail.

Use cases across industries

Edge computing isn’t limited to a single vertical. Whether it’s a hospital, a farm, or a retail chain, the ability to process data locally brings real value to operations.

Smart cities

In urban infrastructure, edge devices help manage everything from traffic lights to public safety systems. With the ability to analyze video feeds and environmental data on-site, cities can reduce congestion, improve air quality, and respond to issues faster. Azure IoT Edge supports these efforts by running localized applications that would otherwise need cloud resources.

Healthcare

Azure IoT Edge devices are helping clinicians deliver faster, smarter care. Devices worn by patients can continuously collect data and trigger alerts when something looks off—all without needing to send everything to the cloud. Local processing enables real time image analysis, which supports quicker diagnostics and more responsive treatment in both hospitals and remote care environments.

Agriculture

In smart farming, edge devices paired with sensors provide up-to-the-minute data on soil health, temperature, and moisture levels. This allows farmers to create AI modules that automate irrigation, improve yield, and adapt in real time to changing weather. When IoT Edge runtime runs on local devices, it enables precise control without constant cloud access.

Retail

In retail, edge device work includes everything from smart shelf tracking to in-store personalization. AI models running on-site can recommend offers, track inventory, and even detect patterns in shopper behavior. Retailers using Azure IoT Edge holds can reduce cloud costs while improving the speed of data insights across every store.

Manufacturing

Factories depend on edge computing to stay ahead of downtime. Edge devices read sensor data, spot wear and tear, and flag issues before machines break down. These same systems can transmit data to dashboards or control systems while processing data locally to make split-second adjustments on the line. This balance of local logic and centralized oversight drives consistent efficiency.

Challenges in implementing edge devices

Challenges

Implementing edge devices in large-scale IoT projects presents several challenges. The upfront infrastructure cost can be significant, especially when deploying a distributed network of edge devices. Additionally, maintaining these devices can be complex, requiring robust management systems to ensure seamless operation. Furthermore, certain low-power edge devices may have limited computational resources, which can restrict their ability to handle complex data processing tasks.

Potential solutions

Modular Edge Devices: Utilizing modular edge devices allows for incremental scaling of deployments, reducing initial costs and enabling gradual expansion as needed.
Centralized Monitoring Platforms: Leveraging centralized monitoring platforms can simplify device management, providing a unified interface for overseeing distributed networks of edge devices.
Hybrid Cloud-Edge Integration: Developing hybrid devices that integrate cloud and edge computing can offer flexibility, allowing businesses to balance local processing with cloud-based resources for more complex tasks.

By addressing these challenges with strategic solutions, businesses can effectively implement edge devices, maximizing their potential to enhance IoT applications and drive innovation.

Useful Resources

Edge computing for retail

Edge Devices

Edge Computing in Financial Services

Edge Computing Examples

Cloud vs edge computing

Edge Computing and AI

AI & Machine Learning

Myth-Busting: Edge Computing Is Only for Replacing Cloud-Based Analytics Methods

Jonny Cameron 14 May 2025

Replacing cloud analytics with edge is a tempting idea. Edge systems are fast, local, and can run independently of a network. So naturally, some assume that once you have edge capabilities, there’s no more need for cloud-based analytics.

But that’s not how modern analytics works.

Edge computing doesn’t replace the cloud. And it definitely doesn’t replace your cloud analytics stack. Instead, it fills a gap that cloud analytics alone can’t cover—bringing real-time insight to the edge, while continuing to feed the cloud with data for deeper, longer-term analysis.

Why the confusion?

Cloud analytics have been the gold standard for years. You collect data, send it to the cloud, and let your analytics platform turn it into charts, dashboards, and decisions. That model still works, especially when you’re looking at historical data or organization-wide trends.

Edge computing sounds like a different world. Suddenly, data is being analyzed on a factory floor, inside a delivery vehicle, or in a retail store. No dashboards. No round trips to the cloud. Just instant feedback from the device itself.

That shift can feel like a replacement. But in reality, it’s a layer, not a swap.

Cloud vs. Edge: Striking the Perfect Computing Balance for Your Business

What edge analytics actually means

Edge analytics is about time and location. Instead of sending raw data off for processing, edge devices analyse it right there, on-site, in real time.

Examples:

A vibration sensor detects a shift in a machine and flags it before failure occurs
A smart shelf tracks product movement and updates local stock counts instantly
A building management system adjusts HVAC settings based on occupancy and temperature data – without waiting on a cloud signal

These insights don’t need to go to the cloud first. They’re local decisions made from local data, right when it matters.

But here’s the catch: they’re still analytics, and more often than not, that data still finds its way into your broader analytics workflows.

The cloud still plays a critical role

Cloud analytics isn’t going anywhere. You still need it for:

Aggregating data from multiple edge locations
Visualising trends across time
Building dashboards for leadership and operations
Running advanced models for forecasting, inventory planning, and more

Edge analytics improves what cloud-based tools can’t always do: act quickly, close to the data source. But it also improves the cloud by filtering and enriching the data before it ever arrives at your central platform.

This means fewer duplicates, cleaner inputs, and more context-aware insights.

Edge and cloud analytics work better together

Here’s how a hybrid analytics workflow might look:

A device captures data and runs local ML inference to detect an event
That event is logged immediately, and action is taken on-site
Periodic summaries are sent to the cloud to populate dashboards or feed other systems
The cloud aggregates this across hundreds of sites for business-wide analysis

With this setup, you’re not duplicating your analytics stack—you’re extending it. Edge becomes the front line for immediate action, and the cloud remains your core for strategy and scale.

Where Simply NUC fits into edge analytics

Simply NUC systems are purpose-built for this kind of hybrid approach.

Our rugged devices, like the extremeEDGE Servers™, can crunch real-time sensor data and feed alerts into local control systems. Meanwhile, more compact models like Cyber Canyon NUC 15 Pro are ideal for retail or smart office environments where light analytics and cloud syncing go hand in hand.

We have systems that support AI inference, run lightweight analytics frameworks locally, and integrate easily with cloud dashboards and platforms.

And with NANO-BMC, you can remotely manage analytics endpoints without sending teams on-site.

You don’t need to rebuild your analytics stack – you just need to extend it smartly.

It’s not a takeover, it’s a team-up

Edge computing isn’t coming for your cloud analytics platform. It’s going to make it better.

By pushing quick, local decisions to the edge and letting the cloud handle long-term insight and coordination, you get the best of both worlds – faster reactions, smarter strategies, and cleaner, more relevant data at every level.

If you’re using cloud analytics today, great. You’re already halfway there. The next step? Let edge take some of the pressure off and help your insights move faster.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge Machine Learning Runs on Powerful, Expensive Hardware

Jonny Cameron 13 May 2025

For years, AI was a resource-hungry technology, associated with massive infrastructure and elite-level hardware. But that thinking doesn’t reflect where edge ML is today.

The truth? You don’t need oversized gear or oversized budgets to run ML at the edge. You just need the right-sized hardware and a clear idea of what your workload actually requires.

Let’s break it down.

Where this myth came from

Machine learning started as a heavy lift. Training large models involved big datasets, serious compute power, and racks of high-performance servers. It made sense that many people associated AI with large-scale setups.

Then edge computing solutions entered the picture. Suddenly, AI was being deployed to remote sites, factory floors, and mobile devices. With that came a common misunderstanding: that you still needed the same level of horsepower, just in a smaller box.

What many teams overlook is the difference between training and inference.

Inference is lighter than you think

Most edge machine learning use cases don’t involve training models from scratch. They focus on inference, which means running a trained model to make decisions or predictions in real time.

This type of processing is far less demanding. Thanks to tools like TensorFlow Lite, ONNX Runtime, and PyTorch Mobile, even complex models can be slimmed down, optimized, and deployed to compact edge devices.

Techniques like quantization and model distillation help reduce model size and improve speed. This makes it possible to run AI tasks on low-power systems without heavy resource demands.

Edge-ready hardware doesn’t need to be overbuilt

Simply NUC’s range of edge-ready devices shows how ML can run efficiently on smaller, more affordable systems.

In commercial or controlled environments, we give you flexibility.

Take the Cyber Canyon NUC 15 Pro. It’s small, quiet, and powerful enough for edge ML tasks like predictive maintenance, in-store foot traffic analysis, or camera-based analytics. With up to Intel Core i7 processors and high-speed DDR5 memory, it delivers reliable performance in a compact footprint.

And if you’re building out a highly scalable deployment where cost, size, and modularity matter, Simply NUC’s Mini PC lineup – including models like Topaz and Moonstone – offers efficient, compact systems ready for AI inference at scale.

Many of these devices also support AI accelerators such as Intel Movidius or NVIDIA Jetson modules. That means you can run hardware-accelerated inference without needing a traditional GPU.

What can you actually run?

Here are just a few edge ML applications that run smoothly on compact, cost-effective Simply NUC devices:

Smart surveillance using AI to detect motion, intrusions, or identify faces
Retail insights from video analytics tracking customer behavior
Predictive maintenance based on sensor readings in manufacturing equipment
License plate recognition for smart parking or gated access
Building automation through occupancy-aware lighting and HVAC control

None of these require a full-scale server or expensive compute stack. You just need the right model, the right tools, and hardware that fits the job.

It’s not about power. It’s about fit.

The biggest shift in edge ML isn’t the hardware itself. It’s the mindset. Instead of asking, “What’s the most powerful device we can afford?”, a better question is, “What’s the most efficient way to run this task?”

Overbuilding hardware wastes energy, drives up costs, and creates more maintenance overhead. That’s not smart infrastructure. That’s just excess.

Simply NUC helps you avoid that trap. Our systems are configurable, scalable, and designed to give you just enough performance for what your use case needs – without overcomplicating your setup.

You can start small and scale smart

Edge machine learning doesn’t need to be complicated or expensive. With today’s tools, lightweight frameworks, and fit-for-purpose hardware, most teams can get started faster and more affordably than they might expect.

Whether you're deploying a single prototype or rolling out across multiple retail locations, there’s no need to overdo it. Choose the right model, deploy it locally, and scale as you grow.

Need help finding the right fit?
Simply NUC offers a full range of edge ML-capable systems – from rugged to commercial, from entry-level to AI-accelerated. If you’re not sure what you need, let’s talk. We’ll help you match your ML workload to the system that makes the most sense for your environment, your budget, and your goals.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge Computing Means the End of the Cloud

Jonny Cameron 9 May 2025

If you've been keeping up with tech trends, you might have encountered the bold claim that edge computing is set to replace the cloud.

It’s an exciting headline, but it’s far from the truth. Sure, edge computing is growing rapidly, and it’s a game-changer in many scenarios. But the idea that it signals the death of the cloud? That’s a myth we’re here to bust.

The reality? Edge and cloud are not rivals. They’re teammates, each playing a specific role in modern IT infrastructures. Get ready as we set the record straight.

If you want to find out more about how edge can support your existing cloud infrastructure. Read our free ebook here.

Why the myth exists

Edge computing solutions have been gaining a lot of attention, with headlines about AI on the edge, real-time analytics, and decentralized processing. And for good reason. Moving data processing closer to where it’s created reduces latency, saves bandwidth costs, and enables faster decision-making.

But as "edge" becomes the buzzword of the moment, some folks have begun to think that edge computing is meant to replace the cloud entirely.

What edge computing really does

Here’s what edge computing is actually about. Imagine sensors on a factory floor, a self-driving car, or a smart display in a retail store. All of them generate data in real-time, and decisions need to be made on the spot. That’s where edge computing works wonders.

By processing data locally, edge solutions reduce the delay (or latency) that happens when information has to make a round trip to a faraway cloud data center. It’s faster, more private, and cuts bandwidth costs. Edge also excels in environments with unreliable connectivity, allowing devices to operate autonomously and upload data later when it’s practical.

Essentially, edge computing is perfect for localized, real-time workloads. But that doesn’t mean the cloud is out of the picture.

Why the cloud still matters

The cloud isn’t going anywhere, and here’s why: The cloud offers unmatched scalability, storage capacity, and centralization. It’s the powerhouse behind global dashboards, machine learning model training, and long-term data storage.

For example, while edge devices might process data locally for immediate decisions, that data often flows back to the cloud for deeper analysis, coordination, and storage. Think predictive models being retrained in the cloud based on fresh, edge-generated data. Or a global retail chain using cloud insights to fine-tune inventory management across multiple locations.

Bottom line? Cloud computing handles the heavy lifting that edge setups can’t. Together, they’re stronger than either one alone.

The real strategy is hybrid

The future of IT infrastructure isn’t a choice of edge or cloud. It’s the smart integration of both. Edge and cloud working together is the ultimate power move.

Here are a few real-world examples of hybrid systems in action:

Edge AI, cloud brains: Real-time decisions like defect detection on a manufacturing line happen locally at the edge. But insights from those detections sync with the cloud for retraining AI models.
On-site monitoring, global oversight: Edge devices monitor systems in remote locations, while the cloud provides a centralized dashboard for company-wide visibility.
Batching for bandwidth: IoT devices collect data offline in areas with poor connectivity, then upload it in bulk to the cloud when a stable connection is available.

Simply put, hybrid setups are about using the right tool for the right job.

How Simply NUC bridges the gap

At Simply NUC, we’re bridging the edge and cloud like never before. Our extremeEDGE Servers™ are built to thrive in localized environments while staying seamlessly connected to the cloud.

Here’s how Simply NUC makes edge-to-cloud integration effortless:

Cloud-ready out of the box: Whether you’re using AWS, Azure, or Google Cloud, Simply NUC edge systems sync with major cloud platforms while remaining fully capable of operating autonomously.
Flexible modular architecture: Our compact systems can be deployed where data is generated, from factory floors to trucks, scaling your edge workforce without overbuilding.
AI-ready hardware: Integrated GPUs and hardware acceleration options mean tasks like vision processing or predictive analytics run efficiently at the edge. Results can then be synced with the cloud for storage or further analysis.
Reliable, rugged systems: Shock-resistant, temperature-tolerant, and fanless designs ensure our products thrive in challenging environments while staying connected to centralized cloud systems.

Whether you need local processing, cloud syncing, or a mix of both, Simply NUC is here to make your edge-cloud strategy as seamless and scalable as possible.

It’s not either/or—but both

Don’t believe the myth that edge will make the cloud obsolete. The truth is that edge computing complements cloud technology, and the smartest IT strategies use both in tandem.

Want to see how edge and cloud can work together in your business? Explore Simply NUC’s edge-ready solutions to discover how we bring speed and flexibility to your infrastructure without sacrificing the power of the cloud.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

AI & Machine Learning

Myth-Busting: Edge AI Is Too Complex to Set Up

Jonny Cameron

Just when you get used to the idea of AI, along comes “Edge AI”.

But that’s the myth. And it’s time we cleared it up.

The truth? Edge AI has come a long way in a short space of time and setting it up is more approachable than most people think.

Why this myth exists in the first place

Because “AI” and “edge computing” are both complex topics on their own, combining them sounds like it would double the effort. Spoiler: it doesn’t anymore.

Edge AI setup isn’t what it used to be (in a good way)

Today, it’s a different world. The tools have matured, the hardware has gotten smarter, and the whole process is a lot more plug-and-play than people expect.

Here’s what’s changed:

Hardware is ready to roll
Devices like Simply NUC’s extremeEDGE Servers™ come rugged, compact, and purpose-built to handle edge workloads out of the box. No data center needed.
Software got lighter and easier
Frameworks like TensorFlow Lite, ONNX, and NVIDIA’s Jetson platform mean you can take pre-trained models and deploy them without rewriting everything from scratch.
You can start small
Want to run object detection on a camera feed? Or do real-time monitoring on a piece of equipment? You don’t need a full AI team or six months of setup. You just need the right tools, and a clear use case.

Real-world examples that don’t require a PhD

Edge AI is already working behind the scenes in more places than you might expect. Here’s what simple deployment looks like:

A warehouse installs AI-powered cameras to count inventory in real time.
A retail store uses computer vision to track product placement and foot traffic.
A hospital runs anomaly detection locally to spot equipment faults early.
A transit hub uses license plate recognition—on-site, with no cloud lag.

All of these can be deployed on compact systems using pre-trained models and off-the-shelf hardware. No data center. No endless configuration.

The support is there, too

Here’s the other part that makes this easier: you don’t have to do it alone.

We’ve helped teams deploy edge AI in manufacturing, healthcare, logistics, retail, you name it. We’ve seen firsthand how small, agile setups can make a huge difference.

Edge AI doesn’t have to be hard

So here’s the bottom line: Edge AI isn’t just for tech giants or AI labs anymore. It’s for real-world businesses solving real problems – faster, smarter, and closer to where the data lives.

Yes, it’s powerful. But that doesn’t mean it has to be complicated.

If you’re curious about how edge AI could fit into your setup, we’re happy to show you. No jargon, no overwhelm, just clear steps and the right-sized solution for the job.

Useful Resources