Cybersecurity

When most people picture AI in action, they imagine endless racks of servers, blinking lights, and the hum of cooling systems in a remote data center. It’s a big, dramatic image. And yes, some AI workloads absolutely live there.

But the idea that every AI application needs that kind of infrastructure? That’s a myth, and it’s long overdue for a rethink.

In 2025, AI is showing up in smaller places, doing faster work, and running on devices that would’ve been unthinkable just a few years ago. Not every job needs the muscle of a hyperscale setup.

Let’s take a look at when AI really does need a data center (and when it doesn’t).

When AI needs a data center

Some AI tasks are just plain massive. Training a large language model like GPT-4? That takes heavy-duty hardware, enormous datasets, and enough processing power to make your electric meter spin.

In these cases, data centers are essential for:

• Training huge models with billions of parameters
• Handling millions of simultaneous user requests (like global search engines or recommendation systems)
• Analyzing petabytes of data for big enterprise use cases

For that kind of scale, centralizing the infrastructure makes total sense. But here’s the thing, not every AI project looks like this.

When AI doesn’t need a data center

Most AI use cases aren’t about training, they’re about running the model (what’s known as inference). And inference can happen in far smaller, far more efficient places.

Like where?

• On a voice assistant in your kitchen that answers without calling home to the cloud
• On a factory floor, where machines use AI to predict failures before they happen
• On a smartphone, running facial recognition offline in a split second

These don’t need racks of servers. They just need the right-sized hardware, and that’s where edge AI comes in.

Edge AI is changing the game

Edge AI means running your AI models locally, right where the data is created. That could be in a warehouse, a hospital, a delivery van, or even a vending machine. It’s fast, private, and doesn’t rely on constant cloud connectivity.

Why it’s catching on:

• Lower latency – Data doesn’t have to travel. Results happen instantly.
• Better privacy – No need to ship sensitive info offsite.
• Reduced costs – Less data in the cloud means fewer bandwidth bills.
• Higher reliability – It keeps working even when the internet doesn’t.

This approach is already making waves in industries like healthcare, logistics, and manufacturing. And Simply NUC’s compact, rugged edge systems are built exactly for these kinds of environments.

Smarter hardware, smaller footprint

The idea that powerful AI needs powerful real estate is outdated. Thanks to innovations in hardware, AI is going small and staying smart.

Devices like NVIDIA Jetson or Google Coral can now handle real-time inference on the edge. And with lightweight frameworks like TensorFlow Lite and ONNX, models can be optimized to run on compact systems without sacrificing performance.

Simply NUC’s modular systems fit right into this shift. You get performance where you need it without the weight or the wait of data center deployment.

The bottom line: match the tool to the task

Some AI jobs need big muscle. Others need speed, portability, or durability. What they don’t need is a one-size-fits-all setup.

So here’s the takeaway: Instead of asking “how big does my AI infrastructure need to be?” start asking “where does the work happen and what does it really need to run well?”

If your workload lives on the edge, your hardware should too.

Curious what that looks like for your business?
Let’s talk. Simply NUC has edge-ready systems that bring AI performance closer to where it matters fast, efficiently, and made to fit.

Useful Resources

Edge computing technology
Edge server
Edge computing for retail

Edge computing platform 
Fraud detection machine learning

Edge computing in agriculture

Fraud detection in banking

What happens when a business tries to use the same hardware setup for every AI task, whether training massive models or running real-time edge inference? Best case, they waste power, space or budget. Worst case, their AI systems fall short when it matters most.

The idea that one piece of hardware can handle every AI workload sounds convenient, but it’s not how AI actually works.

Tasks vary, environments differ, and trying to squeeze everything into one setup leads to inefficiency, rising costs and underwhelming results.

Let’s unpack why AI isn’t a one-size-fits-all operation and how choosing the right hardware setup makes all the difference.

Not all AI workloads are created equal

Some AI tasks are huge and complex. Others are small, fast, and nimble. Understanding the difference is the first step in building the right infrastructure.

Training models

Training large-scale models, like foundation models or LLMs takes serious computing power. These workloads usually run in the cloud on high-end GPU rigs with heavy-duty cooling and power demands.

Inference in production

But once a model is trained, the hardware requirements change. Real-time inference, like spotting defects on a factory line or answering a voice command, doesn’t need brute force, it needs fast, efficient responses.

A real-world contrast

Picture this: you train a voice model using cloud-based servers stacked with GPUs. But to actually use it in a handheld device in a warehouse? You’ll need something compact, responsive and rugged enough for the real world.

The takeaway: different jobs need different tools. Trying to treat every AI task the same is like using a sledgehammer when you need a screwdriver.

Hardware needs change with location and environment

It’s not just about what the task is. Where your AI runs matters too.

Rugged conditions

Some setups, like in warehouses, factories or oil rigs—need hardware that can handle dust, heat, vibration, and more. These aren’t places where standard hardware thrives.

Latency and connectivity

Use cases like autonomous systems or real-time video monitoring can’t afford to wait on cloud roundtrips. They need low-latency, on-site processing that doesn’t depend on a stable connection.

Cost in context

Cloud works well when you need scale or flexibility. But for consistent workloads that need fast, local processing, deploying hardware at the edge may be the smarter, more affordable option over time.

Bottom line: the environment shapes the solution.

Find out more about the benefits of an edge server.

Right-sizing your AI setup with flexible systems

What really unlocks AI performance? Flexibility. Matching your hardware to the workload and environment means you’re not wasting energy, overpaying, or underperforming.

Modular systems for edge deployment

Simply NUC’s extremeEDGE Servers™ are a great example. Built for tough, space-constrained environments, they pack real power into a compact, rugged form factor, ideal for edge AI.

Customizable and compact

Whether you’re running lightweight, rule-based models or deep-learning systems, hardware can be configured to fit. Some models don’t need a GPU at all, especially if you’ve used techniques like quantization or distillation to optimize them.

With modular systems, you can scale up or down, depending on the job. No waste, no overkill.

The real value of flexibility

Better performance

When hardware is chosen to match the task, jobs get done faster and more efficiently, on the edge or in the cloud.

Smarter cloud / edge balance

Use the cloud for what it’s good at (scalability), and the edge for what it does best (low-latency, local processing). No more over-relying on one setup to do it all.

Smart businesses are thinking about how edge computing can work with the cloud. Read our free ebook here for more.

Scalable for the future

The right-sized approach grows with your needs. As your AI strategy evolves, your infrastructure keeps up, without starting from scratch.

A tailored approach beats a one-size-fits-all

AI is moving fast. Workloads are diverse, use cases are everywhere, and environments can be unpredictable. The one-size-fits-all mindset just doesn’t cut it anymore.

By investing in smart, configurable hardware designed for specific tasks, businesses unlock better AI performance, more efficient operations, and real-world results that scale.

Curious what fit-for-purpose AI hardware could look like for your setup? Talk to the Simply NUC team or check out our edge AI solutions to find your ideal match.

Useful Resources

Edge computing technology
Edge server
Edge computing in smart cities

Edge computing platform 
Fraud detection machine learning

Edge computing in agriculture

One of the most common myths surrounding AI applications is that they require a big investment in top-of-the-line GPUs.

It’s easy to see where this myth comes from.

The hype around training powerful AI models like GPT or DALL·E often focuses on high-end GPUs like NVIDIA A100 or H100 that dominate data centers with their parallel processing capabilities. But here’s the thing, not all AI tasks need that level of compute power.

So let’s debunk the myth that AI requires expensive GPUs for every stage and type of use case. From lightweight models to edge-based applications, there are many ways businesses can implement AI without breaking the bank. Along the way, we’ll show you alternatives that give you the power you need, without the cost.

Training AI models vs everyday AI use

We won’t sugarcoat it: training large-scale AI models is GPU-intensive.

Tasks like fine-tuning language models or training neural networks for image generation require specialized GPUs designed for high-performance workloads. These GPUs are great at parallel processing, breaking down complex computations into smaller, manageable chunks and processing them simultaneously. But there’s an important distinction to make here.

Training is just one part of the AI lifecycle. Once a model is trained, its day-to-day use shifts towards inference. This is the stage where an AI model applies its pre-trained knowledge to perform tasks, like classifying an image or recommending a product on an e-commerce platform. Here’s the good news—for inference and deployment, AI is much less demanding.

Inference and deployment don’t need powerhouse GPUs

Unlike training, inference tasks don’t need the raw compute power of the most expensive GPUs. Most AI workloads that businesses use, like chatbots, fraud detection algorithms or image recognition applications are inference-driven. These tasks can be optimized to run on more modest hardware thanks to techniques like:

• Quantization: Reducing the precision of the numbers used in a model’s calculations, cutting down processing requirements without affecting accuracy much.
• Pruning: Removing unnecessary weights from a model that don’t contribute much to its predictions.
• Distillation: Training smaller, more efficient models to replicate the behavior of larger ones.By doing so, you can deploy AI applications on regular CPUs or entry-level GPUs.

Why you need Edge AI

Edge AI is where computers process AI workloads locally, not in the cloud.

Many AI use cases today are moving to the edge, using compact and powerful local systems to run inference tasks in real-time. This eliminates the need for constant back-and-forth with a central data center, resulting in faster response times and reduced bandwidth usage.

Whether it’s a smart camera in a retail store detecting shoplifting, a robotic arm in a manufacturing plant checking for defects or IoT devices predicting equipment failures, edge AI is becoming essential. And the best part is, edge devices don’t need the latest NVIDIA H100 to get the job done. Compact systems like Simply NUC’s extremeEDGE Servers™ are designed to run lightweight AI tasks while delivering consistent, reliable results in real-world applications.

Cloud, hybrid solutions and renting power

Still worried about scenarios that require more compute power occasionally? Cloud solutions and hybrid approaches offer flexible, cost-effective alternatives.

• Cloud AI allows businesses to rent GPU or TPU capacity from platforms like AWS, Google Cloud or Azure, access top-tier hardware without owning it outright.
• Hybrid models use both edge and cloud. For example, AI-powered cameras might process basic recognition locally and send more complex data to the cloud for further analysis.
• Shared Access to GPU resources means smaller businesses can afford bursts of high-performance computing power for tasks like model training, without committing to full-time hardware investments.

These options further prove that businesses don’t have to buy expensive GPUs to implement AI. Smarter resource management and integration with cloud ecosystems can be the sweet spot.

To find out how your business can strike the perfect balance between Cloud and Edge computing, read our ebook.

Beyond GPUs

Another way to reduce reliance on expensive GPUs is to look at alternative hardware. Here are some options:

• TPUs (Tensor Processing Units), originally developed by Google, are custom-designed for machine learning workloads.
• ASICs (Application-Specific Integrated Circuits) take on specific AI workloads, energy-efficient alternatives to general-purpose GPUs.
• Modern CPUs are making huge progress in supporting AI workloads, especially with optimisations through machine learning frameworks like TensorFlow Lite and ONNX.Many compact devices, including Simply NUC’s AI-ready computing solutions, support these alternatives to run diverse, scalable AI workloads across industries.

Simply NUC’s role in right-sizing AI

You don’t have to break the bank or source equipment from the latest data centre to adopt AI. It’s all about right-sizing the solution to the task. With scalable, compact systems designed to run real-world AI use cases, Simply NUC takes the complexity out of AI deployment.

Summary:

• GPUs like NVIDIA H100 may be needed for training massive models but are overkill for most inference and deployment tasks.
• Edge AI lets organisations process AI workloads locally using cost-effective, compact systems.
• Businesses can choose cloud, hybrid or alternative hardware to avoid investing in high-end GPUs.
• Simply NUC designs performance-driven edge systems like the extremeEDGE Servers™, bringing accessible, reliable AI to real-world applications.

The myth that all AI requires expensive GPUs is just that—a myth. With the right approach and tools, AI can be deployed efficiently, affordably and effectively. Ready to take the next step in your AI deployment?

See how Simply NUC’s solutions can change your edge and AI computing game. Get in touch.

Useful resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform 

Edge devices

AI runs on data. The more data you feed into a system, the smarter and more accurate it becomes. The more you help AI learn from good data, the more it can help you. Right?

Mostly, yes. But there’s an often-overlooked piece of the puzzle that businesses can’t afford to ignore. Hardware.

Too often, hardware is seen as just the background player in AI’s success story, handling all the heavy lifting while the data algorithms get the spotlight. The truth, however, is far more nuanced. When it comes to deploying AI at the edge, having the right-sized, high-performance hardware makes all the difference. Without it, even the most advanced algorithms and abundant datasets can hit a wall.

It’s time to bust this myth.

The myth vs. reality of data-driven AI

The myth

AI success is all about having massive datasets and cutting-edge algorithms. Data is king, and hardware is just a passive medium that quietly processes what’s needed.

The reality

While data and intelligent models are critical, they can only go so far without hardware that’s purpose-built to meet the unique demands of AI operations. At the edge, where AI processing occurs close to where data is generated, hardware becomes a key enabler. Without it, your AI’s potential could be bottlenecked by latency, overheating, or scalability constraints.

In short, AI isn’t just about having the right “what” (data and models)—it’s about using the right “where” (scalable, efficient hardware).

Why hardware matters (especially at the edge)

Edge AI environments are very different from traditional data centers. While a data center has a controlled setup with robust cooling and power backups, edge environments present challenges such as extreme temperatures, intermittent power and limited physical space. Hardware in these settings isn’t just nice to have; it’s mission-critical.

Here’s why:

1. Real-time performance

At the edge, decisions need to be made in real time. Consider a retail store’s smart shelf monitoring system or a factory’s defect detection system. Latency caused by sending data to the cloud and back can mean unhappy customers or costly production delays. Hardware optimized for AI inferencing at the edge processes data on-site, minimizing latency and ensuring split-second efficiency.

2. Rugged and reliable design

Edge environments can be tough. Think factory floors, outdoor kiosks or roadside installations. Standard servers can quickly overheat or malfunction in these conditions. Rugged, durable hardware designed for edge AI is built to withstand extreme conditions, ensuring reliability no matter where it’s deployed.

3. Reduced bandwidth and costs

Sending massive amounts of data to the cloud isn’t just slow; it’s expensive. Companies can save significant costs by processing data on-site with edge hardware, dramatically reducing bandwidth usage and reliance on external servers.

4. Scalability

From a single retail store to an enterprise-wide deployment across hundreds of locations, hardware must scale easily without adding layers of complexity. Scalability is key to achieving a successful edge AI rollout, both for growing with your needs and for maintaining efficiency as demands increase.

5. Remote manageability

Managing edge devices across different locations can be a challenge for IT teams. Hardware with built-in tools like NANO-BMC (lightweight Baseboard Management Controller) lets teams remotely update, monitor and troubleshoot devices—even when they’re offline. This minimizes downtime and keeps operations running smoothly.

When hardware goes wrong

Underestimating the importance of hardware for edge AI can lead to real-world challenges, including:

Performance bottlenecks

When hardware isn’t built for AI inferencing, real-time applications like predictive maintenance or video analytics run into slowdowns, rendering them ineffective.

High costs

Over-reliance on cloud processing drives up data transfer costs significantly. Poor planning here can haunt your stack in the long term.

Environmental failures

Deploying standard servers in harsh industrial setups? Expect overheating issues, unexpected failures, and costly replacements.

Scalability hurdles

Lacking modular, scalable hardware means stalling your ability to expand efficiently. It’s like trying to upgrade a car mid-race.

Maintenance troubles

Hardware that doesn’t support remote management causes delays when troubleshooting issues, especially in distributed environments.All these reasons why hardware matters for edge AI.

What does it look like?

Edge AI needs hardware that matches the brain with brawn. Enter Simply NUC’s extremeEDGE Servers™. These purpose-built devices are designed for edge AI environments, with real-world durability and cutting-edge features.

Here’s what they have:

• Compact, scalable

Extreme performance doesn’t have to mean big. extremeEDGE Servers™ scale from single-site to enterprise-wide in retail, logistics and other industries.

• AI acceleration

Every unit has AI acceleration through M.2 or PCIe expansion for real-time inference tasks like computer vision and predictive analytics.

• NANO-BMC for remote management

Simplify IT with full remote control features to update, power cycle and monitor even when devices are off.

• Rugged, fanless

For tough environments, fanless models are designed to withstand high temperatures and space-constrained setups like outdoor kiosks or factory floors.

• Real-world flexibility

Intel or AMD processors, up to 96GB RAM and dual LAN ports, extremeEDGE Servers™ meet the varied demands of edge AI applications.

• Cost-effective right-sizing

Why spend data center-grade hardware for edge tasks? extremeEDGE Servers™ let you right-size your infrastructure and save costs.

Real world examples of right-sized hardware

The impact of smart hardware is seen in real edge AI use cases:

• Retail

A grocery store updates digital signage instantly based on real-time inventory levels with edge servers, delivering dynamic pricing and promotions to customers.

• Manufacturing

A factory detects vibration patterns in machinery using edge AI to identify potential failures before they happen. With rugged servers on-site, they don’t send raw machine data to the cloud, reducing latency and costs.

• Healthcare

Hospitals use edge devices for real-time analysis of diagnostic imaging to speed up decision making without sending sensitive data off-site.

These examples show why you need to think beyond data. Reliable, purpose-built hardware is what turns AI theory into practice.

Stop Thinking “All Data, No Hardware”AI is great, no question. But thinking big data and sophisticated algorithms without hardware is like building a sports car with no engine. At the edge, where speed, performance and durability matter, a scalable hardware architecture like extremeEDGE Servers™ is the foundation for success.

Time to think beyond data. Choose hardware that matches AI’s power, meets real-world needs and grows with your business.

Learn more

Find out how Simply NUC can power your edge AI. Learn about our extremeEDGE Servers™

Useful resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform 

Edge devices

You know what can make or break your AI workflows? Your tools. Even the most talented minds in AI hit roadblocks when their computing hardware can’t keep up with the breakneck pace of innovation. That’s where the NUC 15 Pro Cyber Canyon comes in. This compact computing powerhouse is designed to optimize every aspect of your AI work, wherever that work happens.

Whether you’re running machine learning models, managing edge deployments, or fine-tuning AI solutions at your desk, the Cyber Canyon delivers seamless performance, advanced AI acceleration, and the flexibility to do it all.

Here’s how the NUC 15 Pro Cyber Canyon can transform AI operations for you.

Where performance meets productivity

One of the standout features of the Cyber Canyon is its 99 TOPS of AI acceleration. That’s thanks to the latest Intel® Core Ultra 2 processors. More specifically, the Arrow Lake H with advanced CPU cores, next-gen Intel® Arc GPU, and NPU, which combined elevate performance to new heights in the new AI-computing era. For AI developers, that means local inference, training data models, and deploying neural networks can happen fast, efficiently, and productively. You get to decide where your projects go from there, while reducing the need to rely on cloud resources.

Key Processor Features:

• Dedicated AI cores and Vision Processing Unit (VPU) with 35% faster inference performance vs the previous generation.
• Up to 16 cores (8 Efficiency + 6 Performance + 2 Low-Power Efficiency) with max clock speed ~5.8 GHz.
• Integrated Intel® Arc™ Graphics with Intel® Xe-LPG Gen 12.9, giving up to 64 execution units, supporting up to four 4K or one 8K display.

With up to DDR5-6400 memory and Gen4 NVMe storage, you’ll see reduced bottlenecks and faster model processing, which translates directly to better workflow efficiency.

Keep AI local, secure and efficient

While cloud-based AI has its strengths, there are growing cases where local processing offers unparalleled advantages. The NUC 15 Pro Cyber Canyon allows businesses and developers to keep sensitive data onsite, reducing latency, minimizing cloud costs, and maintaining strict data privacy.

For industries like healthcare, retail, or manufacturing, where security and speed are crucial, Cyber Canyon provides an edge that cloud computing simply can’t match.

Benefits of local AI processing:

• Lower Latency: Immediate responses without waiting for cloud processing
• Enhanced Privacy: Improved security by keeping sensitive data in-house
• Cost Efficiency: Cut down recurring cloud costs while maintaining quality performance

Cyber Canyon can include Intel® vPro® Technology, which ensures enhanced remote manageability and advanced threat detection. IT teams benefit from having a secure, reliable platform for running AI workloads without compromise.

Next-gen connectivity to plug into any workflow

AI workflows don’t exist in a bubble. Often, they require integration with a wider network of devices and processes. Fortunately, Cyber Canyon is built for multi-connectivity.

Future-proofed with the latest Wi-Fi 7 and Bluetooth 5.4, the NUC 15 Pro is built to be a reliable hub for high-speed, next-gen connectivity.

Features like dual Thunderbolt™ 4 ports, HDMI 2.1, abundant USB-A and USB-C I/O, and 2.5Gb Ethernet make Cyber Canyon a seamless fit within any advanced system. Whether you’re connecting external GPUs for tensor operations, processing data from sensors, or managing edge AI devices, this machine is built to handle it all.

It even supports quad 4K displays, making it the perfect device for real-time AI applications requiring visualization or dashboards.

And if your system needs to grow? Cyber Canyon’s tool-less 2.0 tall chassis design makes expansion effortless, providing slots for extra storage or PCIe add-ons.

Compact form, massive potential

Modern AI demands high-powered machines, but it doesn’t demand the bulk of traditional workstations. That’s where the compact design of Cyber Canyon stands out (but not literally, it’s small).

At just 0.48L for the Slim chassis or 0.7L for the Tall chassis, the NUC 15 Pro Cyber Canyon fits anywhere—from cluttered offices to isolated industry deployments. Its MIL-STD-810H certification ensures it can handle harsh environments too. Portable yet powerful, it’s the perfect workstation for labs, edge setups, and corporate offices alike.

And don’t be fooled by its small size. Its performance easily rivals that of full-size desktops, all while staying energy-efficient and whisper-quiet.

Real-World Applications of Cyber Canyon for AI

The NUC 15 Pro Cyber Canyon is engineered to meet the demands of professionals across various industries. Here’s how it excels in real-world scenarios:

1. AI Development and Training

Optimize development cycles with powerful local processing and quick adjustments to models.

2. Edge Computing

Deploy real-time AI inferencing at the edge for IoT applications or industry automation. Evaluate and respond to data instantly without cloud reliance.

3. Healthcare

Process sensitive patient data securely, allowing health facilities to employ AI in diagnostics and treatment recommendations while meeting strict privacy standards.

4. Retail

Provide dynamic, real-time pricing or personalized shopping experiences with instant response powered by on-site AI engines.

5. Media Production and Creative Workflows

For creators working with AI-enhanced video editing, rendering, or content generation, Cyber Canyon’s hardware boosts creativity without delays, ready with the latest Microsoft Copilot out of the box.

Why Cyber Canyon is built for the future of AI

Every component of Cyber Canyon is purpose-built for modern and future AI workflows. By blending high performance, security, and scalability into a form factor designed for versatility, it empowers businesses, developers, and enterprises to push the boundaries of innovation.

Whether you’re fine-tuning an advanced marketing recommendation engine, testing ML models in a lab, or processing sensory input in a factory, Cyber Canyon brings you the ability to do more, faster, and smarter.

Let your AI workflows work better with Cyber Canyon

With the Simply NUC 15 Pro Cyber Canyon, you have a long-term ally designed to help you succeed.

Want to experience the benefits firsthand?

Explore how Cyber Canyon can redefine the way you approach AI.

Useful Resources

Edge computing in agriculture

Edge server

Fraud detection in banking

Sound familiar?

You’re evaluating your hardware options and leaning towards off-the-shelf solutions. Maybe it seems like the safer, more budget-friendly choice. After all, custom hardware gets a reputation for being expensive, right? But what if that assumption isn’t entirely true? Could this be limiting your potential to achieve better performance and cost savings for your business?

Let’s take a look.

The myth of custom hardware costs

The idea that “custom hardware is too expensive” comes from a surface level comparison. Off-the-shelf solutions are built for mass production, often with a lower upfront cost. They appeal to businesses looking for quick and easy solutions. But these solutions often come with hidden costs and limitations that only become apparent after deployment.

Standard hardware is designed for the broadest possible audience, so it’s rarely optimized for your business needs. You may end up paying for features you don’t need or worse, compensating for underpowered capabilities with additional upgrades. That’s where custom hardware shines.

The hidden costs of off-the-shelf solutions

On the surface, off-the-shelf solutions may seem cost effective, but they come with trade-offs that businesses can’t ignore. Here’s what gets overlooked:

1. Paying for features you don’t need

Off-the-shelf solutions are designed for the widest possible range of users. What if your business doesn’t need top end graphics or excessive storage? With standard devices you’ll still pay for those features. Custom hardware lets you invest in only what you need.

2. Underperformance leading to inefficiencies

Has your team experienced slow response times or performance bottlenecks? Standard solutions prioritize broad appeal over specialized functionality so they’re not suited for specific workloads like data analytics, AI model training or industrial automation. This inefficiency can hurt productivity and lead to additional system upgrades or workarounds.

3. Shorter lifespan and higher upgrade costs

Standard solutions are built without future scalability in mind. This means shorter lifespans and businesses have to replace earlier. Custom hardware, tuned to your needs, is better equipped to handle changing demands and extend its lifespan and reduce long term costs.

4. Wasted power and higher operational expenses

Generic solutions have one-size-fits-all power configurations, so you waste energy. For power hungry IT environments this means higher operational costs. By specifying energy efficient components, custom hardware eliminates unnecessary power consumption.

Why custom hardware makes sense

Custom hardware lets businesses invest in optimized performance so every dollar spent contributes to specific goals. Here’s how it benefits you in the long run:

1. Pay for what you need, not for what you don’t

Imagine being able to configure your system with just the processing power, memory and storage you need for your specific workload. Custom hardware gives you that control, so you don’t pay for features or capabilities you don’t use.

2. Performance lowers operational costs

Purpose built hardware means smoother workflows. Highly optimized for specific tasks it minimizes downtime and maximizes efficiency so you save time and operational expenses.

3. Longer lifespan and scalability

Custom solutions aren’t just built for current needs; they’re designed for growth. Modularity and upgradability means your hardware can adapt as your business evolves, reducing the frequency of costly replacements.

4. Energy efficiency for cost savings

By selecting only the components you need for your operations, custom hardware can reduce energy consumption dramatically. This doesn’t just save you money on power bills; it also aligns with sustainability goals, a win-win for cost and corporate responsibility.

5. Simplified IT maintenance

Custom systems are easier to deploy and maintain because they’re built with your existing infrastructure in mind. This reduces the workload for IT departments, saving on labor costs and minimizing downtime.

Real world examples of cost effective custom hardware

To bring this to life here are a few use cases where custom hardware is the smarter financial choice:

AI and machine learning

A mid-sized retailer reduced cloud processing costs by deploying custom AI hardware for edge computing. The solution allowed them to process complex models locally, avoiding exorbitant cloud fees.

Retail and POS systems

A point-of-sale (POS) provider chose custom mini PCs for their terminals, saving on hardware requirements while ensuring operational reliability and compact design.

Healthcare imaging

A hospital upgraded diagnostic imaging equipment with custom configured systems for AI driven diagnostics. This resulted in faster results and cost savings by reducing power consumption.

Industrial automation

An engineering firm deployed ruggedized custom hardware for edge computing to prevent costly downtime in harsh industrial environments.

Simply NUC solutions for businesses looking for efficiency

If you’re considering custom hardware Simply NUC combines technical expertise with cost effective solutions. Our modular, customizable systems are built to your needs so you only pay for what you need.

Here’s what Simply NUC offers:

1. Customizable mini PCs: These systems can be configured with the processing power, memory and storage you need.
2. Scalable performance: Whether you need AI, data analytics or industrial capabilities Simply NUC has systems built for specific workloads.
3. Sustainable and cost efficient designs: Lower energy consumption and upgradable hardware reduces total cost of ownership (TCO).
4. Edge computing solutions: For businesses that need local processing Simply NUC has purpose built infrastructure to minimize cloud dependency and associated costs.

True or False? The myth busted

The myth that custom hardware is too expensive doesn’t hold up. While upfront costs may be higher in some cases, custom hardware can save businesses money in the long run through optimized performance, reduced operational costs and longer life cycles.

Instead of settling for generic solutions that don’t meet specific needs businesses should consider custom hardware as a strategic investment.

Useful Resources

Edge Server

iot edge devices

Edge Computing Solutions

edge computing in manufacturing

edge computing platform

Edge Devices

edge computing for retail

edge computing in healthcare

Edge Computing Examples

Cloud vs edge computing

Edge Computing in Financial Services

To handle AI workloads and multitasking with ease, your system needs a powerful processor and plenty of RAM. Dedicated hardware, like GPUs or an integrated AI chip, is essential for tasks like machine learning, data analysis, and real-time inferencing.

Flexibility is just as important. Your setup should support a variety of operating systems and tools while working seamlessly with AI frameworks like TensorFlow, PyTorch, or OpenAI APIs. A compact design is a big plus, letting it fit into modern workspaces without sacrificing performance.

High-speed connectivity and Bluetooth should keep everything running smoothly. You’ll also want fast SSD storage, energy-efficient performance, and the option to upgrade components.

The NUC 15 Pro Cyber Canyon, developed by ASUS and customized by Simply NUC, can give you all of that.

If you’re comparing options for a powerful yet compact AI-ready system, this guide will help you decide whether Cyber Canyon should power your next project.

Why Cyber Canyon?

The Cyber Canyon uses the latest  Intel’s Core Ultra architecture, featuring an integrated Neural Processing Unit (NPU) and GPU that together deliver up to 99 TOPS of AI acceleration. That means you can run machine learning, local AI inferencing, and data-heavy applications without relying on cloud servers-reducing latency, increasing privacy, and keeping performance close to the source.

Pair that with a small footprint, energy-efficient design, and Windows 11 Pro, Home, or IoT with Copilot integration, and you’ve got a system built for both the AI revolution and day-to-day productivity.

Performance and flexibility in one compact system

Available in Slim (0.48L) and Tall (0.7L) chassis options, Cyber Canyon delivers workstation-grade power in a size that fits anywhere, from a developer’s desk to an edge computing cabinet. Inside, you’ll find:

• Up to 16 cores / 16 threads via Intel Core Ultra CPUs
• Up to DDR5-6400 memory, expandable to 96GB
• PCIe Gen5 NVMe storage support, up to 10TB
• Integrated Intel Arc Graphics for creative and visual workloads
• AI acceleration through CPU + GPU + NPU working in tandem

And thanks to Thunderbolt 4, HDMI 2.1, multiple USB-A and USB-C ports, and a 2.5Gb Ethernet connection, you get seamless high-speed connectivity with support for up to four 4K displays.

Built to do more-with less

The NUC 15 Pro compact design makes it ideal for tight workspaces and edge deployments, but it’s not just about size. The system is also MIL-STD-810H certified, meaning it’s rugged enough for industrial environments. An advanced cooling system keeps things running 24/7, while its energy-efficient architecture helps reduce power consumption without sacrificing performance.

Out-of-the-box AI and productivity

Cyber Canyon can be customized with Windows 11 Pro, Home or IoT – all of which support Microsoft Copilot, your built-in AI assistant for productivity, search, and content creation. Prefer Linux or want to roll your own setup? No problem. Cyber Canyon is also compatible with Ubuntu, and other popular Linux distributions, or you can opt for a barebones build to configure the OS and software stack your way.

How Cyber Canyon compares to other AI mini PCs

If you’re considering other AI-optimized small form factor systems, like the Apple Mac Mini M4, Cyber Canyon stands out in several key areas:

• Wider OS compatibility: Windows, Linux, and more
• Hardware flexibility: Choose your RAM, storage, and OS with a tool-less 2.0 design for easy upgrades
• Designed for both AI and beyond: More than a creative workstation, ideal for development, enterprise IT, and edge computing
• Up to 99 TOPS of AI Performance: Nearly 3x more than the advertised 38 TOPS of Apple’s M4 chip
• Built for the future: With the latest generation of Wi-Fi 7 and Bluetooth 5.4, Cyber Canyon offers unmatched value in its class

Cyber Canyon lets you tailor your system around your specific needs-whether that’s prototyping machine learning models or powering a remote signage solution.

Who should consider Cyber Canyon?

This system is built for users who want a future-ready platform for AI and beyond. It’s ideal for:

• Developers and AI researchers who need high-performance local compute
• Small and medium-sized businesses (SMBs) looking to scale efficiently
• Creative professionals managing visual workloads
• Edge deployments in retail, healthcare, manufacturing, and logistics
• IT teams who need remote management and robust security via Intel vPro

Compare Cyber Canyon to your current system

If you’re relying on traditional PCs-or high-end workstations with limited flexibility-it might be time to rethink your setup. Cyber Canyon delivers performance that rivals larger systems in a footprint that fits anywhere, with the added benefit of AI-readiness baked in.

Configure your AI PC today

Choose from pre-configured models or customize your own:

• Core i3 with 16GB RAM, 256GB SSD
• Core i5/U5/vPro with 32GB RAM, 512GB SSD
• Core i7/U7/vPro with 32GB RAM, 1TB SSD

The Tall chassis adds support for extra drives, a second Ethernet port, or expansion modules-perfect for enterprise or evolving projects.

Ready to bring next-gen computing into your workspace?

Configure your Cyber Canyon now and discover the power of compact AI computing.

The truth is, AI is not restricted to the cloud and can indeed operate without it, thanks to edge computing capabilities.

Let’s take a deeper look at the misconception and explore where the cloud fits into the AI ecosystem, and how edge computing offers a new approach to running AI workloads.

The traditional relationship between AI and the cloud

It’s no secret that cloud computing has been integral to the development and deployment of AI solutions. With features such as scalable storage, immense computing power, and centralized data processing, the cloud often feels synonymous with AI. The cloud enables AI models to process vast amounts of data, train on centralized datasets, and serve global institutions that have geographically distributed teams.

The benefits of the cloud for AI

• Scalable storage 

The cloud provides the ability to store and process massive datasets, a critical requirement for training machine learning models.

• Centralized accessibility 

Distributed teams can seamlessly collaborate using shared cloud applications, promoting efficient AI development.

• Computing power 

Cloud platforms deliver robust computational resources without requiring businesses to invest in expensive on-premise hardware.

The downsides of running AI in the cloud

While the cloud is indispensable in many ways, it comes with limitations that challenge its effectiveness for specific AI workloads.

• Latency issues 

Cloud processing introduces delays, which can be problematic in applications that require real-time responsiveness, such as autonomous vehicles or live medical diagnostics.

• Bandwidth costs 

Frequent and sizable data transfers to and from the cloud can lead to costly bandwidth expenses.

• Data privacy concerns 

Some businesses operating in fields like healthcare or finance worry about entrusting sensitive data to third-party cloud providers, due to security and regulatory risks.

These challenges raise an important question. If relying entirely on the cloud creates these hurdles, is there an alternative?

Introducing edge computing

Edge computing processes AI tasks closer to the data source, such as IoT devices, sensors, or local servers, without the need for constant back-and-forth communication with the cloud. This localized processing allows businesses to address many of the drawbacks associated with cloud dependence.

Why businesses are moving AI workloads to the edge

1. Ultra-low latency 

By running AI operations in real-time at the edge, latency is dramatically reduced. This capability is vital for industries like healthcare (e.g., AI-assisted diagnostics) and manufacturing (e.g. predictive maintenance).

2. Cost efficiency 

Edge computing eliminates the need for continuous data transfer to the cloud, reducing bandwidth usage and saving costs in the long run.

3. Stronger data security 

Keeping sensitive data on-site minimizes the risk of exposing proprietary or confidential information to third-party infrastructure. This is an especially important solution for industries like healthcare, where HIPAA regulations demand stringent data security.

4. Reliable operations 

Edge computing allows organizations to maintain AI functionality even during cloud outages or network disruptions, which is critical in high-stakes environments like factories or hospitals.

Real-world examples of edge computing in action

• Manufacturing: Factories are using AI-powered predictive maintenance systems right on the production floor, enabling them to anticipate machinery failures without needing cloud connectivity.
• Retail: AI checkout systems process customer transactions in real time, delivering a seamless shopping experience unhindered by external latency.
• Healthcare: Diagnostic tools with edge-based AI capabilities analyze medical imaging locally, providing instant feedback to clinicians while maintaining patient data privacy.

Through these use cases, it’s clear that edge computing is not just a theoretical alternative but a viable and increasingly critical solution.

Hybrid AI approaches

It’s important to note that edge computing doesn’t aim to replace the cloud entirely. Instead, the two technologies can work in harmony, creating a hybrid model that combines the best of both worlds. Businesses leveraging hybrid AI models can process sensitive or time-critical workloads locally through edge computing while utilizing the cloud for broader data storage, model training, or long-term analytics.

For example, smart security camera systems often process live video streams locally on the device (edge computing) to identify immediate threats. Summarized insights from these streams are then sent to the cloud for further analysis or storage.

This hybrid approach ensures flexibility, efficiency, and scalability for various applications while balancing the strengths of each technology.

The idea that AI only works in the cloud is simply false. While the cloud continues to play a critical role in AI development and deployment, edge computing offers a powerful alternative for businesses seeking efficiency, security, and real-time responsiveness. For industries with specific latency, cost, or security needs, edge computing isn’t just an option; it’s a necessity.

For organizations looking to adapt AI to their unique needs, this evolution signifies exciting new opportunities. Whether you’re running AI exclusively on the edge or adopting a hybrid model, the possibilities are endless.

If your organization is considering ways to implement AI beyond the cloud, learn how Simply NUC’s edge computing solutions can tailor AI systems to your business requirements.

For more on cloud how edge computing gives cloud a helping hand, read our ebook.

Useful Resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform 

Edge devices

Meet your ultimate fraud detection tool: edge computing

When businesses first consider implementing artificial intelligence (AI), off-the-shelf hardware is often seen as the obvious choice. It’s easy to source, typically affordable, and often sufficient for general-purpose computing. For organizations taking their first exploratory steps into AI projects, choosing widely available hardware might feel like a logical, low-risk decision.

But when AI applications advance beyond basic workloads, the cracks in this approach start to show. While off-the-shelf hardware has a role to play, relying solely on it for complex AI tasks can limit your organization’s ability to scale, optimize, and fully unlock the value of AI.

This article examines the advantages of generic hardware, its limitations for demanding AI workloads, and the benefits of tailored hardware solutions, helping you evaluate the best fit for your AI needs.

The appeal of off-the-shelf hardware for general tasks

Generic, off-the-shelf hardware has long been a staple in IT departments for a variety of reasons. Here’s why it’s a popular choice:

• Affordable and accessible: These products are widely available and competitively priced, making them ideal for organizations prioritizing budget over performance.
• Ease of setup: They come ready to use, with minimal technical expertise required to get started.
• Versatility: Off-the-shelf systems are suitable for basic computing tasks, such as running standard productivity software, emails, and file storage.
• Vendor support: Large hardware vendors typically offer robust support networks, which businesses can rely on for troubleshooting and replacements.

For companies experimenting with basic AI models or testing initial use cases, these benefits can make off-the-shelf hardware a tempting choice. For example:

• A small retail business might use generic hardware to analyze historical sales data with simple algorithms.
• A startup might explore entry-level machine learning frameworks on consumer-grade GPUs.

However, while off-the-shelf systems can handle these initial experiments, they often fall short as AI projects become more sophisticated.

Why generic hardware fails for advanced AI applications

AI workloads are resource-intensive, often requiring more power, scalability, and precision than generic hardware can provide. Here are some of the key limitations of off-the-shelf systems:

1. Performance bottlenecks

AI applications, especially those involving deep learning or neural networks, demand high computational power. Off-the-shelf hardware often lacks the necessary performance capabilities, leading to slower processing speeds and increased latency. This can be particularly problematic for:

• Real-time applications like object detection in autonomous vehicles.
• Tasks requiring immediate data analysis, such as financial fraud detection.

2. Lack of scalability

As organizations deepen their commitment to AI, their hardware needs will inevitably grow. Off-the-shelf hardware is rarely designed with scalability in mind, making it difficult to expand infrastructure without replacing entire systems. This limitation can hinder long-term growth and innovation.

3. Inefficient energy consumption

AI workloads can run continuously over extended periods, consuming significant energy. Without optimizations for AI-specific tasks, generic hardware often operates at lower efficiency, leading to higher operational costs.

4. Limited support for specialized tasks

Advanced AI applications often involve workloads that require tailored configurations, such as high-bandwidth memory or specialized accelerators like GPUs or TPUs. Off-the-shelf systems often lack these features, making it difficult to achieve optimal performance.

For enterprises handling complex workloads such as advanced predictive analytics, real-time image processing, or edge computing, these limitations can quickly result in diminished productivity, unnecessary costs, and the inability to compete effectively in an increasingly AI-driven market.

The case for tailored hardware in AI workloads

To overcome the challenges of generic hardware, many organizations are turning to tailored solutions designed specifically for AI workloads. Tailored hardware provides highly targeted features and configurations to meet the unique needs of AI applications. Here’s why it’s the preferred choice for serious AI initiatives:

1. Enhanced performance

Tailored hardware solutions are optimized to handle the heavy computational loads AI applications require. For instance:

• Dedicated GPUs or TPUs process data faster and more efficiently than consumer-grade hardware.
• Systems designed for AI can handle vast datasets, enabling faster training and inference speeds.

2. Cost optimization

While tailored hardware might seem like a bigger upfront investment, it often leads to better long-term ROI. With configurations designed specifically for AI workloads, organizations avoid the inefficiencies of underused generic hardware or the need to purchase additional systems to meet performance demands.

3. Scalability

Tailored solutions allow businesses to grow their infrastructure as their AI needs evolve. For example, modular designs enable companies to add more computing nodes or specialized accelerators without a complete overhaul. This flexibility supports innovation while protecting initial investments.

4. Custom configurations

Unlike generic hardware, tailored solutions can be fine-tuned to meet the specific demands of an organization. Whether it’s customized memory bandwidth or AI accelerators for unique workloads, these solutions provide a level of precision generic systems cannot match.

Examples of tailored AI solutions in action

The benefits of purpose-built hardware solutions for AI are already being realized across industries. Here are just a few examples of how customizable systems outperform their off-the-shelf counterparts:

• Manufacturing: Real-time quality control systems use AI to analyze production line data and identify defects instantly. Tailored hardware ensures these systems operate efficiently without delays that could disrupt operations.
• Retail: Advanced customer behavior analytics rely on vast datasets to deliver hyper-personalized recommendations. Customized AI hardware enables the rapid processing of these datasets, ensuring retailers offer seamless shopping experiences.
• Healthcare: High-performance diagnostic tools use tailored AI systems to analyze medical imaging data while complying with strict privacy regulations. This ensures fast, accurate diagnoses that improve patient outcomes.

These examples highlight how organizations across sectors are using tailored hardware to unlock the full potential of AI.

Off-the-shelf hardware may seem “good enough” for AI at a glance, but the reality is that it often struggles to support the complexity and resource demands of modern AI workloads. For businesses serious about AI, tailored hardware solutions provide the performance, scalability, and efficiency needed to achieve maximum impact.

Still unsure whether tailored hardware is the right fit for your organization? Take the next step by evaluating your specific AI workloads and determining your long-term goals. For expert advice and solutions tailored to your unique needs, contact Simply NUC today.

Useful Resources

Edge server

Edge computing for beginners

Edge computing in simple words

Computing on the edge

Edge computing platform 

Edge devices

Meet Your Ultimate Fraud Detection Tool: Edge Computing

When you hear the term edge computing, what comes to mind? For many, the image is clear: rugged devices in remote oil rigs, agricultural fields, or mining sites. These are the scenarios often highlighted in case studies and industry presentations, and understandably so. Edge computing excels in these environments, where traditional cloud computing may falter due to connectivity challenges or harsh conditions.

However, while edge computing thrives in rugged locations, focusing solely on its use in these scenarios is a limited perspective. The reality is that edge computing offers substantial benefits across a variety of industries and operational contexts, including urban, healthcare, retail, and even traditional office settings.

Why the myth persists

The belief that edge computing is exclusively for rugged or remote contexts stems from its most publicized use cases. High-profile examples often include industrial or remote-site deployments where robust, weather-resistant devices are critical to ensuring a system’s reliability.

Industries like agriculture, mining, and energy have led the way in leveraging edge computing. For instance:

• Remote Oil Rigs use edge devices to process data locally, minimizing the need to transfer massive amounts of data to central servers.
• Agriculture applications often feature IoT sensors monitoring soil conditions, weather patterns, and crop health in vast, disconnected fields.
• Mining Operations lean on edge computing to enhance safety and efficiency in environments where real-time data processing is non-negotiable.

While these examples showcase the importance of rugged edge hardware, they’ve inadvertently pigeonholed edge computing as a niche solution for extreme conditions, overshadowing its versatility and scalability for broader applications.

The broader reality of edge computing

Edge computing isn’t just about ruggedness or overcoming physical constraints. Its true value lies in its ability to process data closer to its source, reducing latency, increasing operational efficiency, and enhancing security. These benefits are universal and applicable across almost every modern business sector.

Real-time decision-making across industries

One of the most compelling advantages of edge computing is the ability to process data in real-time, making it crucial for applications where decisions need to be made instantly. Consider these everyday examples:

• Urban Data Centers leverage edge computing to manage enormous amounts of data generated by IoT devices across smart cities.
• Retail Outlets use edge technology for real-time inventory monitoring and personalized customer experiences.
• Healthcare Facilities integrate edge computing for patient monitoring and diagnostics, enabling quicker and more accurate clinical decisions.

Enhanced security and data privacy

For industries with stringent data regulations or security concerns, edge computing allows sensitive data to be processed locally rather than being transmitted over networks to the cloud. This approach minimizes vulnerabilities and aligns with privacy regulations in sectors such as finance, healthcare, and retail.

Operational efficiency in traditional environments

Operational efficiency isn’t limited to harsh conditions. For example:

• Manufacturing Plants use edge computing for predictive maintenance and real-time process automation, ensuring minimal downtime.
• Smart City Infrastructure employs edge devices for traffic management, public safety enhancements, and energy-efficient systems.

These versatile applications show that edge computing can address challenges faced by both digital-first enterprises and businesses entrenched in more traditional operational models.

Real-world examples of edge computing

Edge computing has made a significant impact in non-rugged, commercial environments. Below are some examples that highlight its diverse applications:

• Retail 

Edge computing drives smart inventory management by processing sales data in real-time, ensuring stock is always available. For customers, it powers in-store analytics to offer personalized promotions and seamless shopping experiences.

• Healthcare 

 Hospitals utilize edge devices for monitoring patients in real-time, which can be lifesaving in critical situations. Additionally, processing diagnostic data locally ensures compliance with privacy regulations like HIPAA.

• Manufacturing 

 Manufacturers employ edge computing for predictive maintenance by monitoring equipment performance and addressing issues before they lead to failures. Real-time adjustments during production can improve quality assurance.

• Smart Cities 

 By enabling real-time traffic management and public safety monitoring, edge computing is paving the way for smarter, more efficient urban living. It also supports energy-efficient systems for infrastructure like streetlights and smart grids.

Simply NUC as a versatile edge computing partner

When it comes to deploying edge computing solutions tailored to specific operational needs, Simply NUC provides versatile and scalable hardware. By offering adaptable solutions, Simply NUC ensures that edge computing deployments are effective in various contexts, from bustling urban landscapes to traditional office environments.

For instance, lightweight and compact edge devices from Simply NUC can power in-store retail analytics or provide real-time medical insights in a hospital setting, showing the breadth of edge computing’s potential beyond remote or industrial applications.

Edge computing is everywhere

The myth that edge computing is only useful for rugged or remote locations is officially busted. While these environments have made effective use of edge computing, its capabilities extend far beyond. Enterprises in sectors like retail, healthcare, manufacturing, and urban development are reaping the benefits of edge computing to enhance decision-making, strengthen security, and boost operational efficiency.

If you’re considering integrating edge computing into your operations or want to learn how it can be tailored to your specific needs, we encourage you to explore the possibilities. Contact us to discuss how edge computing can drive value for your business.

Useful Resources

Edge computing for retail

Edge computing for small business

Edge computing in healthcare

Edge computing in manufacturing

Edge computing in smart cities

Edge computing in financial services

Edge computing for agriculture and smart farming