AI has moved from experiment to everyday tool. Employees use AI assistants to summarize documents, draft emails and translate calls, and more and more businesses want to build their own AI applications on top of internal data. That raises a practical infrastructure question for IT managers and business owners: where should this AI actually run? On employees' laptops, on a dedicated AI system under the desk, or in the cloud?
Picture a 40-person tax consultancy. The partners want AI to pre-sort incoming client documents and draft standard letters. Staff already use a free cloud chatbot, sometimes with client data that should never leave the company. And one tech-savvy employee has asked for an NVIDIA DGX Spark to "build our own model." That's three requests with three very different hardware answers.
This guide compares the three main options (AI PCs, compact AI workstations such as workstations with NVIDIAs GB10-Chip, and cloud AI services) and helps you decide which combination fits your business.
The Business Challenge: Everyone Wants AI, but Not Everyone Needs the Same Hardware
AI workloads differ enormously. Summarizing a meeting is a small task. Customizing a language model with ten years of project documentation is a large one. Running a chatbot for thousands of customers is something else again. Hardware that suits one of these jobs is often wasted money for another.
Overspending
Expensive specialist hardware sits idle because nobody has the skills or a clear use case.
Shadow AI
Employees paste sensitive data into free consumer tools because no approved alternative exists.
Unpredictable costs
Cloud usage fees grow faster than planned as adoption spreads across the company.
Why does that matter? Many AI features run continuously in the background during the workday, from noise suppression and background blur in video calls to live captions and smarter search. If the CPU or graphics chip had to handle all of this, laptops would run hotter, slow down and drain their batteries faster. The NPU takes over these tasks with far less energy, which leaves the rest of the system free for the actual work.
For Microsoft's Copilot+ PC category, devices need an NPU with at least 40 TOPS, 16 GB of RAM and a 256 GB SSD. Current processor generations comfortably exceed that. Intel's Core Ultra Series 3 offers a 50 TOPS NPU, AMD's Ryzen AI 400 series reaches 60 TOPS, and Qualcomm's Snapdragon X2 chips deliver 80 TOPS. Intel expects roughly one in two new PCs to be an AI PC in 2026. For most businesses, the question is therefore not whether AI PCs arrive, but when, which is usually with the next hardware refresh.
What All GB10 Workstations Have in Common
Every system in this category is built on the NVIDIA GB10 Grace Blackwell Superchip, which combines a Blackwell GPU with a 20-core Arm CPU. It delivers up to 1 petaFLOP of AI performance with 128 GB of unified memory and supports models of up to 200 billion parameters. Two systems can be connected to work with models of up to 405 billion parameters. The whole system measures just 150 × 150 mm. It comes preloaded with NVIDIA DGX OS and the NVIDIA AI software stack, including tools such as PyTorch and Jupyter.
Why the memory matters: Large AI models only run if they fit into memory. A typical professional graphics card offers a fraction of that capacity. In GB10 systems, the 128 GB is shared between the CPU and GPU, so they can load models that standard workstations simply can't handle.
Dell, HP or Lenovo: Where the Models Differ
Because the core specifications are identical, choosing a manufacturer is less about performance and more about how the system fits into your existing IT:
Storage: Configurations range from 1 TB to 4 TB. Dell offers 1, 2 or 4 TB, while HP offers 1 TB or 4 TB. If your team works with several large models and datasets, 4 TB saves a lot of housekeeping. With 1 TB, it makes sense to keep models and data on a NAS or server.
Vendor software: HP adds its ZGX Toolkit, which bundles open-source frameworks, MLflow experiment tracking, Ollama testing and simple export for deployment. HP also designs the system to work as a network-connected AI resource for existing Windows, Mac or Linux devices. Dell emphasizes that models can move easily between the desktop and Dell data center environments. Lenovo positions the ThinkStation PGX as a secure sandbox that keeps intellectual property on your premises.
Service and support: The usual business service options apply. Dell, for example, offers ProSupport with next-business-day onsite repair after remote diagnosis.
CANCOM tip: For most comapnies, the simplest rule is to choose the manufacturer that already equips your device fleet. One vendor means one support contract, one procurement process and one point of contact.
The biggest advantage is reach. The most capable AI models need computing power on a scale no desk system can match, and in the cloud you can use them from day one without buying, installing or maintaining hardware. Billing is per user license or based on actual usage. That makes cloud AI particularly attractive for pilot projects, fluctuating workloads and companies that want to roll out AI to many employees quickly.
Cloud AI services range from Microsoft 365 Copilot to AI platforms on Azure and other providers. The key question is less whether to use the cloud than which data you entrust to it, which the limitations below address.
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At a Glance: AI PC vs. AI Workstations vs. Cloud AI
How to Choose the Right Solution
Before you invest, answer five questions:
Who will use AI? If it's everyone, prioritize AI PCs and a company-wide cloud service. If it's a few specialists, consider a dedicated workstation.
How sensitive is your data? Client files, personal data and intellectual property are strong arguments for local processing.
Do you want to use AI or build it? Using AI points to AI PCs plus cloud. Building and customizing models points to DGX Spark.
How predictable is your usage? Constant, heavy use makes owning hardware pay off. Occasional use favors the cloud.
Do you have the skills in-house? A DGX Spark only delivers value if someone can work with Linux and AI frameworks.
CANCOM recommendation: For most SMBs, the answer isn't either/or but a hybrid approach. Make AI PCs the standard with every hardware refresh. Provide an approved cloud AI service for company-wide assistants. Add a DGX Spark-class system only where a team actually builds or customizes models with sensitive data. Start with the use case, not the hardware.
Practical Tips for Getting Started
Run a pilot first. Test one clearly defined use case for three months and measure the result, for example hours saved per week.
Classify your data. Define which information may go to cloud tools and which must stay in-house. A clear AI usage policy is the most effective way to stop shadow AI.
Don't under-spec memory. Treat 16 GB of RAM as the minimum for AI PCs and 32 GB for power users. In many slim business notebooks, memory is soldered and can't be upgraded later.
Plan the surrounding infrastructure. A local AI system needs a fast network, reliable backup and proper access control, just like any server.
Calculate total cost of ownership. Compare three years of cloud fees with the full cost of owned hardware, including energy, administration and support.
Conclusion
There is no single "best" AI infrastructure, only the right mix for your business. AI PCs bring everyday AI features to every employee and are becoming standard with each hardware refresh. Compact AI workstations like NVIDIA DGX Spark give developers and specialist teams the power to build and customize large models locally, with sensitive data kept in-house. Cloud AI offers maximum scale and the most capable models without upfront investment. Businesses that start with clear use cases, classify their data and combine these options deliberately get the full benefit of AI without overspending on hardware they don't need.