How to Choose a Dedicated GPU Cloud Provider

Why Choosing a Dedicated GPU Provider Is Harder Than It Looks

Most enterprise AI teams start looking for dedicated GPU infrastructure after a specific experience on shared capacity convinces them they need something more predictable. It might be a training run whose throughput changed from one day to the next with no change to the code, a security review that hit a roadblock over who else was running on the same hardware or a quarterly bill that finance had no way to forecast.

That experience is now common enough to show up in industry research. Broadcom's Private Cloud Outlook 2025 found that 55% of IT leaders prefer private cloud for AI model training, tuning and inference, and that 69% are considering moving workloads from public to private cloud, with a third having already done so.

The difficulty is that almost every GPU cloud now calls some part of its offering dedicated, and the term means very different things from one provider to the next. These are usually commitments of a year or more, so an evaluation built on headline GPU counts and hourly rates can lock a team into infrastructure that solves the wrong problem. The questions below separate dedicated infrastructure from renamed shared capacity.

1. Make Every Provider Define "Dedicated" in Writing

In the GPU cloud market, "dedicated" can describe reserved GPU VMs drawn from a shared pool, dedicated hosts that still sit on a shared fabric and storage backend, bare-metal servers on a multi-tenant switch, or fully single-tenant clusters built for one customer. Only the last of these removes other customers from the components that determine how your workloads behave.

The most useful question you can ask is which parts of the environment are shared with other customers. Sharing a data centre building, power, cooling and public internet egress is normal and harmless. The components that matter are the GPU fabric, the storage layer and the compute itself. A provider offering true single-tenant GPU infrastructure should state plainly that none of these is shared between customers. It should also commit in writing to zero oversubscription, because a fabric or storage system sold beyond its full-rate capacity will make your performance move with other customers' behaviour, regardless of what the contract calls the environment.

2. Evaluate Isolation Where It Affects Performance and Security

Isolation at the GPU fabric layer has a direct performance consequence for distributed training. Collective operations such as all-reduce run only as fast as the slowest link involved, so congestion from a neighbouring tenant on a shared InfiniBand or Ethernet fabric shows up as stalls in your step time. The result is benchmark variance, where the same job produces different throughput on different days and your team can no longer trust the architectural decisions it makes from those numbers.

Isolation at the storage and compute layers has an equally tangible security consequence, since AI workloads concentrate proprietary training data, model weights and often regulated personal data in one place. IBM's Cost of a Data Breach Report 2026 puts the global average cost of a breach at a record $4.99 million, with AI-driven breaches averaging around $6 million. Single-tenant infrastructure does not make a breach impossible, but it removes an entire category of questions from the InfoSec review because there is no shared-tenancy exposure left to assess.

3. Decide Where You Want the Responsibility Line to Sit

Hardware isolation tells you who else is on the cluster but it says nothing about who operates it. That second question shapes your team's workload more than most buyers expect, because every layer the provider does not run becomes a layer your engineers must build, patch and support for the length of the contract.

Map each provider's offering against the layers of the stack, from physical custody through network and storage, the operating system and orchestration with Kubernetes or SLURM, up to a self-service layer where users request GPU VMs through a portal or API. A bare-metal handoff suits AI labs that want full control of drivers and kernels, but for most enterprise teams it creates a platform project nobody budgeted for. Ask whether the provider can move the handoff point to suit your team and whether taking on more of the stack also brings a stronger SLA.

4. Treat the SLA as an Engineering Question

An uptime percentage tells you how much the provider will refund when things go wrong, not how quickly your workloads will recover. The real mechanism behind any GPU SLA is standby hardware, and the type of standby a provider holds determines how long a failed node keeps your cluster degraded.

• Hot standby means spare nodes are online, powered and configured, so a provider can swap out a failed node in minutes and sometimes automatically.

• Warm standby means spares are racked and configured but not yet patched into the fabric, so recovery takes hours.

• Cold standby is the baseline posture and relies on on-site stock that must be unboxed and provisioned or on an OEM warranty replacement, which can take days to weeks.

Ask which levels are available at which service tier, who carries out hardware swaps and where the support engineers are based, because a help desk that can action a node swap directly is very different from a ticket queue waiting on a hardware vendor.

5. Test the Virtualisation Objection Instead of Assuming It

Many infrastructure teams insist on bare metal because they assume virtualisation always costs performance. That assumption made sense when virtualised GPUs meant emulated devices and software-defined networking in the data path, but it should be tested with each provider rather than accepted as a fixed rule.

Ask whether GPUs and network interface cards are passed through directly to the virtual machine, whether the fabric runs at bare-metal line rate and who tunes the drivers and kernels. A provider that has done this work properly can demonstrate parity with bare metal and the payoff is significant because VMs bring faster reprovisioning, cleaner separation between internal teams and self-service access for researchers. If the performance cost is zero, refusing VMs on principle means giving up those advantages and getting nothing in return.

6. Match Storage and Networking to Your Actual Workloads

A provider that proposes the same design to every customer is optimising for its own operations rather than your workloads. A well-designed cluster runs a backend east-west fabric for GPU-to-GPU traffic, a frontend north-south network for internet and storage access and an out-of-band network that monitors hardware health independently of the workload path.

Storage deserves the same scrutiny as the network design, and general-purpose systems such as Ceph are flexible and cost-effective for VMs, backups, object storage and multi-tenant use, though they are not the most performant option by default. Demanding training workloads benefit from parallel file systems such as WEKA, VAST or DDN, which are RDMA-capable and let GPUs talk directly to storage. A strong provider will recommend one or the other based on your workload profile and explain the cost and performance trade-off in plain terms.

7. Price the Contract on Utilisation, Not on Hourly Rate

On a fixed contract of a year or longer, the hourly equivalent rate matters far less than the proportion of capacity your organisation actually uses, because idle GPUs cost the same as busy ones. Ask whether you can divide the cluster between departments, place workloads dynamically and repurpose idle capacity for research or spot-style jobs, since these capabilities turn one large commitment into a shared internal resource with a predictable cost per useful GPU-hour.

Dedicated builds are designed for sustained, large-scale demand, so the honest first question is whether your usage justifies one yet. Teams running short experiments or dealing with highly variable demand are often better served by on-demand GPU VMs until their usage pattern supports a long-term commitment.

A Checklist for Comparing Dedicated GPU Cloud Providers

Put the same questions to every provider on your shortlist so that the answers can be compared side by side.

Why Choose Hyperstack Dedicated Cloud

We offer Dedicated Cloud as one of the deployment models within our Secure Private Cloud, which is dedicated, single-tenant GPU infrastructure with zero oversubscription on contracts of 12 months or longer. With Dedicated Cloud, we deploy Hyperstack and AI Studio directly onto your own cluster and run almost the entire stack for you, which gives you:

· Self-service access to your dedicated NVIDIA GPUs as GPU VMs through the same portal and API used for our public-cloud regions.

· The highest SLA of our Secure Private Cloud deployment models, backed by hot, warm and cold standby hardware.

· Performance that is virtualised, not compromised, with GPUs running at 100% of bare-metal performance inside our GPU VMs and network interface cards passed through using SIOV for full line rate.

· A fixed monthly cost with the flexibility to split capacity across departments and repurpose idle GPUs for spot or R&D use.

· A staffed 24/7/365 help desk where our engineers action hot-node swaps directly.

The Provider You Choose Decides What Your Engineers Spend Their Year On

A dedicated GPU contract decides whether your engineers spend the next year on models or on infrastructure, whether your benchmarks can be trusted and whether your security and finance teams can sign off with confidence. Pay closest attention to the answers that stay vague, because vagueness about tenancy, standby hardware or the consumption model usually points to a gap the provider would rather not discuss.

If you are evaluating dedicated GPU infrastructure for training or inference at scale, speak to the Hyperstack team about how Dedicated Cloud would be sized around your workloads.

FAQs

What should I look for in the best dedicated GPU cloud providers?

Look for a written statement of what is shared with other customers, zero oversubscription, a choice of consumption models, an SLA backed by named standby hardware and in-house support that can replace failed nodes directly.

What is the difference between dedicated GPU VMs and single-tenant GPU infrastructure?

Dedicated GPU VMs from many providers reserve a server for you while the fabric and storage may still be shared, whereas single-tenant GPU infrastructure such as our Secure Private Cloud isolates the GPU fabric, storage and compute for one customer.

Are GPU VMs on an enterprise GPU cloud slower than bare metal?

They do not have to be when the provider passes devices through directly and tunes the stack, and on Hyperstack GPUs run at 100% of bare-metal performance inside GPU VMs and often better.

How large does a dedicated GPU deployment need to be?

At Hyperstack, the typical minimum for a dedicated build is 512+ NVIDIA GPUs, which represents roughly 64 systems, on contracts of 12 months or longer.