On-device AI vs cloud AI vs private-hosted AI
These are three different places a model's inference can actually run: on-device means the model executes locally, on the hardware already in the user's phone, laptop, or edge box; cloud AI means it runs on a shared, multi-tenant API operated by a vendor and reached over the internet; private-hosted AI means it runs on infrastructure dedicated to one organization, whether that's owned hardware or a rented, exclusive machine. Each one moves the same basic tradeoff, between capability, cost, latency, and data exposure, to a different place.
On-device AI
On-device AI runs the model directly on the hardware already in front of the user: a phone's neural processing unit, a laptop's GPU, or an edge device with its own local compute. Nothing leaves the device to answer a request, which removes network latency and keeps data local by construction, not by policy. The tradeoff is capacity: the model has to fit in whatever memory and compute budget the device actually has, which today rules out the largest and most capable models on most consumer hardware. On-device deployments tend to use smaller, more heavily quantized models suited to a narrower set of tasks, and they inherit whatever compute the device shipped with rather than scaling to the workload.
Cloud AI
Cloud AI is the default most people mean by "using an LLM": a request goes over the internet to a vendor's API, which runs it on a shared pool of GPUs alongside requests from every other customer. Isolation between customers is enforced in software, at the request level, not by giving each customer separate hardware. This model gives access to the largest and most capable models without owning any hardware, and usually bills per token consumed rather than per unit of time. The tradeoff is that your data passes through infrastructure you don't control, under whatever data-handling terms the vendor offers, and your requests share capacity with everyone else's traffic on that endpoint.
Private-hosted AI
Private-hosted AI sits between the other two: the model runs on infrastructure dedicated to one organization, not shared with other tenants, but it isn't necessarily running on the end user's own device either. That dedicated infrastructure can be owned hardware in the organization's own building, or a machine rented from a provider and allocated exclusively, with no other customer's workload sharing the same GPU. Because the hardware is dedicated, an organization running private-hosted AI can see and control exactly what's running, which model, which logging, which network boundary, in a way a shared cloud API doesn't expose. It generally costs more in fixed terms than paying per token on a cloud API at low volume, since the infrastructure has to be paid for whether or not it's busy, but it removes the multi-tenant exposure that comes with a shared API. See our explainer on what makes an LLM deployment actually private for the distinction between this and a vendor's "private tier" of its shared API.
Where each one fits
On-device suits latency-sensitive, narrowly scoped tasks where the device's own hardware is enough, and where no request should ever leave the device at all. Cloud AI suits workloads that need the largest available models, don't carry data sensitive enough to require dedicated hardware, and benefit from paying only for tokens actually used. Private-hosted AI suits workloads where the data or the workload itself justifies dedicated hardware, whether for control over what's running, predictable flat-rate cost at sustained volume, or a straightforward answer to a compliance question about who else's traffic touches the machine.
Where GPUwerk fits
GPUwerk rents dedicated DGX Sparks, in our EU-Central cloud or shipped to a customer's own office, so it sits in the private-hosted category: no other tenant's workload runs on your Spark while it's yours, whether we operate it or you run it on-premise. More on the on-premise option is on our on-premise page, and on the hosted option on our private LLM hosting page.