If you are not yet among them, it’s only a matter of time before you join. Businesses across almost every industry are rolling out AI-driven features as core parts of their products and internal operations.
In 2024, enterprise spending on generative AI jumped over 6× (to ~$13.8B) compared to 2023, signaling a shift from pilot projects to full execution of AI strategies.
In short, AI has become mission-critical, and this wave of adoption is profoundly affecting VPS hosting trends and the infrastructure choices companies are making. So what does this have to do with VPSs, some of which don’t even offer GPUs?
AI hosting demand is changing VPS not just from the outside, but from the inside too. Offers are becoming more specialized, focusing on things like higher I/O throughput or expanded NVMe storage. And that’s exactly what you need for AI projects.
You used to be able to buy a cloud instance with a GPU or a dedicated GPU server and happily rely on those resources to move your model forward. But today, GPUs aren’t always the most important piece. Some AI server workloads can run on CPUs, and at a certain point, this becomes a cost-effective way to separate compute.
More data means more servers, that part is obvious. The real question is: which servers? You’re not going to keep buying dedicated GPU machines every time you hit a bigger workload. That creates an extra challenge for businesses: building a sensible infrastructure that matches both budget and actual resource needs. That’s why you’ll increasingly see AI startups running not only a GPU server or instance, but also a couple of VPS boxes alongside it.
As one cloud survey put it, “Many [organizations] prioritize the reliable and cost-effective operation of pre-trained models” over building new algorithms from scratch. In other words: self-hosted AI with fewer headaches.
The dominant AI workloads today are things like inference (running models to get predictions or generate content), integrating AI into features (e.g., customer support chatbots, recommendation engines, code assistants), and automating tasks — rather than pure research or model development.
Of course, you don't need the highest-end server with a GPU for this.
Companies no longer need a PhD research lab to leverage AI. You can grab an off-the-shelf model and deploy it using tools like Hugging Face’s model hub and efficient inference libraries. This democratization means a long tail of developers and startups running AI workloads on their own servers, rather than relying solely on a few cloud AI APIs.
VPS for AI is often a plan-based solution with a better price-to-performance ratio for steady, moderate workloads compared to fully managed AI cloud services. You get the same resources, but with cloud-like scalability — without always paying cloud-like premiums.
VPS is more controlled and adjustable. It gives raw control over infrastructure, unlike the constrained environments of managed services. You control the resources, pricing, and data on the server, in compliance with your privacy policies.
And finally, VPS options are accessible in more ways than one.
GPUs are great at handling complex math, especially when the same operation runs thousands of times in parallel. CPUs, on the other hand, are better at handling a bunch of different tasks at once and working through complex logic.
Even though small LLM inference can work on both CPU and GPU, you need to divide several processes so as not to load the one and only server with a GPU.
Most practical, business-level AI workloads don’t require specialized accelerators and can run just fine on CPUs.
If a service has a small user base, an expensive GPU machine sits idle 90% of the time, burning your money. And when you suddenly get a traffic spike, that same machine can’t keep up — so everyone ends up waiting for hours.
In production, a model typically serves user requests in real time, where stability and response time matter most. That’s usually a better fit for a multi-core CPU server than a GPU box.
Training large neural networks really does benefit from GPUs — otherwise, it could take years. But a lot of the supporting steps, like data preprocessing, classic machine learning algorithms, and inference, often scale better on low-latency CPU servers.
In practice, it’s pretty simple: heavy training tasks run on a dedicated GPU, and lighter workloads stay on regular CPU servers. The resource you’re more likely to scale or upgrade immediately is the GPU, not a couple of CPU VPS instances, which are easy to scale on demand anyway.
A Virtual Private Server is no longer just a bargain host for your blog or app (though you can still use it for that).
Specialized GPU nodes and CPU server clusters are now standard tools for teams that aren’t burning through budgets and can scale up calmly when they need to. And hosting providers offer solid-value VPS options that no longer feel like “cheap hosting.”
Right now, we’re seeing providers morph their offerings to include GPU VPS hosting, high-performance storage, or AI-ready software stacks. Businesses flock to these flexible servers to deploy their machine learning models. So, AI isn’t just something VPSs host; it’s becoming part of the service quality, too.