Use case · AI & GPU compute
Deep learning
Deep learning trains and runs neural networks, which are computationally intensive and GPU-accelerated. Beyond GPUs, it needs a working software stack — frameworks like PyTorch and TensorFlow with matching drivers — and infrastructure spanning experimentation, training, and inference. Dedicated GPU servers suit sustained deep-learning work, hosted in the EU for data sovereignty.
Key points
- Deep learning runs neural networks, which are GPU-accelerated because of their parallel mathematics.
- Beyond hardware, it needs a working software stack — frameworks, drivers, and matching versions.
- The work spans experimentation, training, and inference, each with different infrastructure needs.
- Interactive experimentation benefits from GPUs available on demand to the people iterating.
- Sustained deep-learning work favours dedicated GPU servers, hosted in the EU for sovereignty.
What is deep learning as a workload?
Deep learning is the branch of machine learning that uses neural networks with many layers to learn from data, and it underlies much of modern AI — from image and speech recognition to the large language models now in wide use. As a computing workload, deep learning is defined by the heavy, highly parallel mathematics of these neural networks: training a network adjusts its many parameters over large amounts of data, and running a trained network applies that same mathematics to produce results. Both are computationally intensive in a way that suits specialised hardware, which is why deep learning and GPUs are so closely associated.
Hosting deep-learning work means supporting this whole practice, not a single task. Deep learning involves developing and experimenting with models, training them, and then running them in production, each of which places its own demands on infrastructure. It also depends on a particular software stack to run at all. So infrastructure for deep learning has to provide the GPU compute the mathematics needs, the software environment the frameworks require, and support across the lifecycle from experimentation to production. This page looks at deep learning as a whole; the specifics of large training runs and of production serving are covered in more depth on the training and inference pages.
Why GPUs are the foundation of deep learning
The mathematics of neural networks is dominated by operations that can be performed in parallel across large arrays of numbers, and GPUs are built precisely for such massively parallel computation. Where a CPU handles a modest number of operations at once, a GPU performs many thousands in parallel, which is exactly what the matrix and tensor operations at the heart of deep learning require. This is why GPUs accelerate deep learning so dramatically compared to CPUs, and why serious deep-learning work — beyond the smallest models and datasets — is done on GPUs. The GPU is the foundation on which deep-learning performance rests.
This dependence means that specifying deep-learning infrastructure starts with the GPUs: how much GPU memory is available, how many GPUs, and how capable they are, since these determine what models can be worked with and how quickly. The considerations that apply to GPU selection for training and inference — memory to fit the model, performance for speed, interconnect for multi-GPU work — apply to deep learning generally, because they follow from the GPU being central to the workload. Providing deep-learning infrastructure therefore means providing appropriate GPUs first, with the rest of the system built to support and keep them working.
The deep-learning software stack
Deep learning does not run on bare hardware alone; it runs on a software stack, and getting that stack right is a real part of hosting the workload. At the top are the frameworks — such as PyTorch and TensorFlow — in which models are built and run. Beneath them sit the libraries and drivers that let the frameworks use the GPUs, and these must match: a framework version expects particular GPU driver and library versions, and mismatches cause failures or lost performance. So a working deep-learning environment is a coherent stack of framework, libraries, and drivers whose versions align, on hardware the drivers support.
This makes the software environment a genuine consideration in deep-learning infrastructure, not an afterthought. A GPU server is only useful for deep learning once it has a correct, working stack, and keeping that stack consistent — so that work is reproducible and not derailed by version mismatches — is part of running deep-learning infrastructure well. Approaches such as containerised environments help by packaging the stack into reproducible units, so the same environment can be relied on across machines and over time. When we host deep-learning infrastructure, having the hardware is only the start; the environment on it has to actually run the frameworks the work depends on, which is something to get right rather than assume.
Infrastructure across the deep-learning lifecycle
Deep-learning work moves through phases, and the infrastructure that suits each differs. In experimentation and development, people iterate on models interactively — trying ideas, debugging, running small jobs — which benefits from GPUs being readily available to those doing the work, with quick turnaround. In training, models are trained on larger data for extended periods, which is the sustained, heavy workload where dedicated GPUs and good data throughput matter most. In inference, trained models are served in production, where latency, throughput, and availability come to the fore. One deep-learning effort typically passes through all three.
Supporting the whole lifecycle means providing infrastructure appropriate to each phase, rather than treating deep learning as a single kind of workload. The training and inference phases have their own detailed considerations, covered on the dedicated training and inference pages; the experimentation phase, less often discussed, has its own needs around interactive access to GPUs. Hosting deep-learning work well means recognising which phase a given need falls into and providing for it — interactive GPU access for development, sustained dedicated GPUs for training, and available, reliable serving for inference. We can host across this lifecycle, sizing each part to its role.
Development and experimentation infrastructure
The experimentation phase of deep learning deserves attention because it is where much of the work happens and where infrastructure needs differ from production. Developing models is iterative: a practitioner tries an idea, runs it, examines the result, and adjusts, often many times, frequently in interactive tools such as notebooks. This benefits from GPUs being available to the people doing the work with little friction, so that iterations are quick and experimentation is not held up waiting for hardware. Slow or scarce access to GPUs during development drags on the whole process, since it is the tight loop of trying and adjusting that drives progress.
Providing for this can mean giving development teams access to dedicated GPU capacity for their experimentation, so they can iterate without contention or delay. Because experimentation is often less predictable and less continuously heavy than production training, how it is provisioned can differ — but the aim is the same: responsive access to GPUs for the people building the models. A dedicated GPU server available to a team for development gives them the interactive, low-friction access that fast iteration needs. When we host deep-learning infrastructure, this development phase is part of what we provide for, not only the production workloads, because much of the value of deep-learning work is created in that experimental loop.
Data and storage for deep learning
Deep learning is data-driven — models learn from and are applied to data — so storage and data handling are part of its infrastructure. Datasets for deep learning can be large, and during training they must be read and delivered to the GPUs fast enough to keep them working, so fast storage and a capable data path matter, as they do for training generally. Beyond training, there is data to manage across the lifecycle: datasets, model checkpoints, trained models, and results, all of which need somewhere to live and to be accessible where the work runs.
Providing for this means pairing the GPU compute with storage suited to the data: fast storage close to the GPUs to feed training without bottlenecking, and capacity to hold the datasets and model artefacts the work produces. Underprovisioning storage or data throughput undercuts the GPUs, which then wait on data, so the storage side is part of a balanced deep-learning system rather than a separate concern. When specifying deep-learning infrastructure, we size the storage and data path alongside the GPUs, so that the data the models learn from and produce is handled as capably as the computation itself.
Bare metal, cloud, and sovereignty for deep learning
For deep learning, the choice between dedicated bare-metal GPU servers and on-demand cloud follows the same logic as other GPU workloads, applied across the lifecycle. Sustained work — ongoing training, steady development by a team, production inference — keeps GPUs busy and is usually more economical on dedicated hardware at a fixed cost than on hourly cloud rates. Intermittent or bursty needs, and short experiments, can suit on-demand capacity. Much deep-learning work by an active team is sustained enough that dedicated GPUs are the economical foundation, with on-demand capacity useful for occasional bursts.
Sovereignty applies to deep learning as it does across AI work, because the data involved is often sensitive. Training data, the data models are applied to, and the models themselves can be confidential or regulated, and where they are processed determines the legal protections and access that apply. VV Internet Hosting is incorporated in the Netherlands, within the EU, so deep-learning work hosted with us runs under European jurisdiction and outside the direct reach of the US CLOUD Act. For deep-learning work on sensitive or regulated data — which much of it is — keeping the data and compute together in the EU keeps them under European law, which is a real consideration in choosing where the work runs.
Where VV Internet Hosting fits — and where it does not
We host dedicated GPU infrastructure for deep learning across its lifecycle: GPUs sized for the models being worked with, a software environment that runs the frameworks the work depends on, fast storage to feed training and hold artefacts, and interactive access for development — in EU datacenters under European jurisdiction. This suits teams doing sustained deep-learning work, from experimentation through training to production, where dedicated GPUs are economical and where the data involved makes European sovereignty matter. For that, we are a strong fit, and we will size the GPUs, storage, and environment to the actual work.
We are clear about our limits. Occasional, bursty experimentation that needs GPUs for short spells only can be well served by on-demand cloud, and we will say so. If you need a hyperscaler's managed deep-learning platforms, vast on-demand GPU fleets, or a specific proprietary ecosystem of managed services, that is a different kind of provider than we are. We are for dedicated, sovereignty-aware deep-learning infrastructure on hardware you control, suited to sustained work — which describes a great deal of serious deep-learning practice, though not every case. If it matches your work, we can host it well; if not, we will point you toward what fits.
Related use cases
Questions
Deep learning, answered plainly
Common questions about hosting for Deep learning.
Why does deep learning need GPUs?
The mathematics of neural networks is dominated by operations performed in parallel across large arrays of numbers, and GPUs are built for exactly this massively parallel computation — performing many thousands of operations at once where a CPU handles far fewer. This makes GPUs accelerate deep learning dramatically over CPUs, so serious deep-learning work is done on them.
What software does deep learning need, beyond hardware?
A working stack: frameworks like PyTorch or TensorFlow in which models are built, plus the libraries and GPU drivers beneath them — and these must match, since a framework version expects particular driver and library versions. A GPU server is only useful for deep learning once it has a correct, coherent stack, often kept reproducible with containerised environments.
Is dedicated or cloud infrastructure better for deep learning?
It depends on how sustained the work is. Ongoing training, steady team development, and production inference keep GPUs busy and are usually more economical on dedicated hardware at a fixed cost. Occasional or bursty needs, and short experiments, can suit on-demand capacity. Much active deep-learning work is sustained enough that dedicated GPUs are the economical foundation.
Planning Deep learning infrastructure?
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