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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Yes—small businesses can run some AI models on local computers or servers, provided the hardware and software support the workload. This can reduce reliance on cloud inference, but it does not automatically lower total costs or electricity use. The answer depends on the model, usage, equipment, power, and operating work involved. This guide is about inference—using a trained model—not training one.
What running an AI model locally means
With local inference, a model runs on a computer or server your business controls rather than sending each request to a cloud model endpoint. Microsoft says its Foundry Local software can perform inference on-device without a cloud dependency after the model has been downloaded and cached; that describes Foundry Local specifically, not every AI runtime. See Microsoft’s Foundry Local FAQ.
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Local processing can keep prompts and other inputs on that device, but it does not make an application secure by itself. Your business still needs to manage software updates, compatibility, vulnerabilities, and access. In a local-first application that can fall back to a cloud service, establish when information leaves the device and how the cloud endpoint handles it.
Which hardware and workloads can run locally?
There is no single hardware specification that guarantees a good result for every model. Relevant resources include the CPU, GPU or NPU, memory, and storage, and the runtime must support the hardware. Smaller models are generally more suitable for device execution; a larger model may exceed available resources. Even if a model loads, it may be too slow or limited for the business task.
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Evaluate the actual application rather than treating “it runs” as “it is ready for work.” Test the model’s answer quality, latency, throughput, memory use, context length, and performance with the number of simultaneous users you expect. Microsoft’s hardware guidance for Windows AI discusses device resources, but a suitable configuration remains workload- and runtime-specific.
Does local AI avoid cloud power costs or save money?
Local inference can reduce requests billed by a cloud provider, but it shifts equipment and operational costs to your business. A meaningful comparison includes the equipment’s acquisition cost over its useful life, how much it is used, electricity for the complete system, any cooling, maintenance and support, and the cloud charges for the same workload and service level.
There is no universal break-even point in the cited documentation. Microsoft reported an estimate of 0.16–0.60 watt-hours per typical query to some of its largest and most capable LLMs in a June 15, 2026, Cloud Blog post. The vendor says the estimate varies by query length, model, and datacenter specifications. It is a cloud-inference estimate—not a measurement of local inference or a direct comparison for a small business. The post is “Scaling AI with 8 to 20x energy efficiency”.
To compare options for your business, gather estimates for one defined workload and use the same assumptions for local, cloud, and hybrid setups:
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitches- Workload: requests per day or month, typical input and output length, busy periods, and simultaneous users.
- Capability: model quality and context length needed to complete the task reliably.
- Performance: measured response time and throughput on the local device and over the network.
- Local costs: equipment cost and useful life, utilization, whole-system electricity, cooling where applicable, maintenance, support, and staff time.
- Cloud costs: the provider’s charges for the matching model, usage, and service level.
- Operating needs: privacy, compliance, security, reliability, scaling, and responsibility for updates.
Without those inputs, a savings percentage or payback period would be guesswork. The cited sources do not establish a representative small-business workload benchmark or a general local-versus-cloud electricity comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a local, cloud, or hybrid setup fits
| Approach | May fit when | Trade-offs to assess |
|---|---|---|
| Local | A supported model on available hardware handles the task at acceptable quality and speed. | Hardware cost, utilization, electricity, maintenance, security updates, and limits on scaling. |
| Cloud | The task needs a model or capacity the business cannot adequately run locally. | Usage charges, connectivity, data handling, and the provider’s service and availability terms. |
| Hybrid | Most tasks are suitable for a supported local model, but some need cloud capability. | Define fallback conditions, cloud data handling, cost controls, and what happens when local inference is unavailable. |
Microsoft Learn describes a common hybrid pattern: try a local model first, then use a cloud endpoint if the model is unavailable, the device is unsupported, a user does not consent to downloading a model, or the task needs a larger model. See “Choose between cloud-based and local AI models”. A fallback preserves an option for harder requests, but it also means some requests may still incur cloud costs and transmit data.
A workstation is not automatically a shared AI service
A model running on one employee’s computer does not by itself provide a dependable service for a team. Microsoft says Foundry Local is not designed for multi-user server inference. A shared endpoint needs capacity management and brings network, security, and availability requirements. See Microsoft’s Local AI Inference for Windows Server.
Before serving multiple people, test expected concurrency and throughput on the intended deployment, and plan how it will be monitored, secured, updated, and kept available. Do not assume that a desktop capable of serving one person will meet a team’s workload.
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