Compute · GPU comparison
Bare metal GPU vs cloud GPU: utilization decides it.
Cloud GPU is elastic and billed per hour — ideal when your GPUs sit idle much of the month. Bare metal GPU is a single-tenant machine with the full hardware, no virtualization overhead, and no egress fees — cheaper once the GPUs run most of the time. The honest deciding factor is utilization: below a few hundred busy hours a month, cloud usually wins; above it, bare metal does. VV Internet Hosting runs the bare-metal side, and will tell you when cloud fits better.
In short
- Utilization is the decision. Cloud wins for bursty, low-utilization work; bare metal wins once the GPUs run most of the month.
- Cost behaves oppositely. Per-hour cloud billing versus flat monthly bare metal — with a crossover around a few hundred busy hours.
- Performance favours bare metal. The whole GPU, no virtualization overhead, full NVLink, and no noisy neighbours.
- Egress is the hidden cloud cost. GPU work moves big data; cloud bills it per gigabyte, bare metal doesn't.
- Cloud still wins sometimes. For spiky, experimental, or elastic workloads, pay-as-you-go is genuinely the better call — and we'll say so.
Two ways to rent a GPU, and what really separates them
A bare metal GPU server is a single physical machine, with its accelerators, that is entirely yours for the duration — no hypervisor, no other tenants, the full hardware at your disposal. A cloud GPU instance is a virtualized slice of a hyperscaler's infrastructure, billed by the hour or second, that you spin up and release on demand. Both give you access to powerful GPUs; the difference is whether you hold a dedicated machine at a flat cost or rent elastic capacity on a meter. Almost every specific trade-off follows from that one distinction.
Because bare metal is a whole machine that is yours, its cost is flat regardless of how hard you run it, its performance is the hardware's full capability without a virtualization layer, and your data sits local with no egress meter. Because cloud is metered elastic capacity, it costs nothing while idle but everything while running, it adds the overhead of virtualization and sometimes shares a physical GPU across tenants, and it bills you to move data out of its network. Neither is inherently better — they are two different economic and performance shapes, and the right one depends entirely on how you actually use GPUs.
That is why this comparison resists a universal winner. A team running a single experiment for a weekend and a team training models around the clock are asking genuinely different questions, and the same honest analysis points them to opposite answers. The useful move is not to declare bare metal or cloud superior, but to find where your workload sits on the two axes that decide it — utilization and data movement — and let those place you. The rest of this page is that placement exercise.
The cost crossover, made concrete
The clearest way to decide is arithmetic. Cloud charges you per hour the GPU runs; bare metal charges a flat monthly rate whether it runs or not. Divide the bare-metal monthly cost by the cloud hourly rate and you get the number of busy hours a month above which bare metal is cheaper — the crossover. For high-end GPU nodes it usually lands well below a full month of continuous use.
# Cloud GPU: pay per hour — great when the GPU sits idle most of the month
cloud 8xH100 ~hourly rate x 730 hrs/mo (always on) = the expensive way to run 24/7
plus egress $ per GB out # moving big datasets/checkpoints adds up
# Bare metal GPU: flat monthly — the machine is yours whether busy or idle
bare-metal 8xH100 = flat monthly no per-hour meter, no egress fee
# crossover: above roughly a few hundred hours/month, bare metal winsThe figures are illustrative and move with the market, so treat the shape rather than the numbers as the point — but the shape is decisive. A per-hour model is cheap while you are idle and grows expensive exactly as you use the hardware more, while a flat monthly model rewards keeping the GPUs busy. Add cloud egress — the per-gigabyte charge to move datasets and checkpoints out — and the crossover shifts further in bare metal's favour for any data-heavy workload. For sustained GPU work, owning the machine for a month is usually cheaper than renting it by the hour to run it all month.
Where does bare metal win on performance?
On the demanding workloads, and for concrete architectural reasons. A single-tenant machine gives you the entire GPU with no hypervisor between your code and the hardware, so there is no virtualization overhead and no noisy neighbour contending for host memory bandwidth or PCIe lanes. For multi-GPU work, you get full NVLink and NVSwitch bandwidth between the accelerators, and for multi-node training you can run over dedicated InfiniBand — the interconnect that keeps large distributed jobs from stalling on communication. These are the details that separate a benchmark from a real training run.
Cloud GPU instances can be excellent, but they make different trade-offs. A hypervisor sits in the path, some instance types partition a physical GPU across tenants, and the quality of the interconnect varies by tier — top-end networking is often reserved for the premium instances. For a lot of workloads none of this is decisive, and cloud performs perfectly well. But for large-model training, tightly-coupled multi-GPU jobs, or latency-sensitive inference where consistency matters, the undivided hardware and full interconnect of bare metal produce faster, more predictable results — which, over a long training run, compounds into real time and cost saved.
Where is cloud GPU the right choice?
When your workload is bursty, experimental, or needs to scale elastically. If you spin GPUs up for a short training run and release them, if your demand spikes unpredictably, if you are prototyping and want no commitment, or if you occasionally need many GPUs briefly and then none, cloud's on-demand model fits that shape far better than fixed capacity. You pay only for the hours you use, you scale to what a moment needs, and you carry no idle machine between bursts. For low-utilization or spiky work, that is not just convenient — it is genuinely cheaper.
We say this plainly because we host bare metal and an honest comparison has to resist recommending it to everyone. If your GPUs would sit idle most of the month, a flat bare-metal rate makes you pay for time you do not use, and cloud is the sounder choice. The point of this page is not that bare metal is always right — it is that utilization decides, and a genuinely elastic or occasional workload belongs in the cloud. Many teams run both: bare metal for the steady baseline, cloud for the spikes. If your pattern is mostly spikes, cloud is where you should be, and we will tell you so.
The hybrid pattern most serious teams land on
In practice, the sharpest answer is rarely all bare metal or all cloud — it is both, matched to the two parts of a real workload. Most teams doing serious GPU work have a steady baseline of utilization, the training or inference that runs more or less continuously, and on top of it a spiky layer of experiments, bursts, and occasional large parallel jobs. The baseline lives naturally on bare metal, where its constant use is cheapest and its performance is fullest; the spikes live naturally in the cloud, where elasticity absorbs them without paying for idle hardware between.
This split resolves the false choice the comparison seems to pose. Rather than forcing an elastic workload onto fixed hardware, or paying cloud's always-on premium for a steady baseline, you put each part where its economics work. The result is usually cheaper than either pure approach: the flat bare-metal cost covers the hours you would always be using anyway, and the cloud meter only runs for the genuine peaks. It also keeps your most data-heavy, performance-sensitive work — the sustained training — on the machine with no egress meter and full interconnect, while the cloud handles the bursts where its convenience is worth the rate.
The reason we can recommend this honestly is that we host the bare-metal half and have no interest in pretending it should carry your spikes too. When we size GPU infrastructure with a team, the first thing we look for is that baseline-plus-spikes shape, because naming it tells us how much belongs on dedicated hardware and how much should stay elastic. If your workload is nearly all baseline, bare metal is almost all of the answer; if it is nearly all spikes, cloud is; and if it is the usual mix, the right design uses each for what it is good at rather than making one do both badly.
Side by side
Bare metal GPU and cloud GPU, dimension by dimension
Read the utilization, performance, and egress rows together — they decide almost every case.
| Bare metal GPU (with us) | Cloud GPU | |
|---|---|---|
| What it is | Single-tenant physical GPU server, full hardware yours | Virtualized GPU instance on shared hyperscaler infra |
| Pricing model | Flat monthly — busy or idle | Per-hour (or per-second); pay only while running |
| Cost at high utilization | Cheaper once the GPU runs most of the month | Expensive when always-on; the meter never stops |
| Cost at low utilization | You pay for idle time | Cheaper for bursty or occasional workloads |
| Performance | Full GPU, no virtualization overhead, full NVLink | Virtualization overhead; sometimes partitioned GPUs |
| Multi-GPU / interconnect | Full NVLink/NVSwitch; InfiniBand for multi-node | Varies by instance; top interconnect on premium tiers |
| Data & egress | Data local; no per-GB egress fee | Egress billed per GB — moving data costs |
| Elasticity | Fixed capacity you provision | Scale up and down on demand |
| Jurisdiction | EU (Dutch B.V.) or US, labelled | Usually US-owned hyperscaler |
| Best for | Sustained training/inference at steady load | Bursty, experimental, or spiky workloads |
Rates, interconnect, and egress vary by provider and instance; verify current figures for your workload before deciding.
The crossover
Where does your workload sit?
Below the crossover, cloud's pay-as-you-go wins; above it, bare metal's flat cost and full performance win — and egress pushes the line further toward bare metal.
We host the bare-metal side, so read that as disclosed bias. The honest rule is simple: below the crossover, cloud's elasticity usually wins; above it, bare metal wins on cost and hands you the full performance and local data besides.
Choosing
How to decide — and how we help
A short, honest path from your utilization and data patterns to the right GPU infrastructure — including cloud.
- 01
Estimate your monthly GPU hours
The decision is mostly utilization. Count how many hours a month the GPU will actually be busy — that single number drives the cost comparison more than any rate.
- 02
Add egress to the cloud side
Cloud GPU bills data transfer out per gigabyte. If you move large datasets or checkpoints, add that to the hourly cost — it is often the hidden half of the bill.
- 03
Weigh performance sensitivity
If you need full NVLink bandwidth and no virtualization overhead — large-model training, latency-sensitive inference — bare metal's undivided hardware matters.
- 04
Value elasticity honestly
If your workload is spiky or experimental and idle much of the month, cloud's pay-as-you-go is genuinely cheaper and simpler — we will say so.
- 05
Let us size the fit
We host bare metal GPUs, so treat that as disclosed bias — and we will point you to cloud when your utilization or burstiness makes it the better answer.
Questions
Bare metal vs cloud GPU, answered plainly
The things compute buyers email us about most.
Is bare metal GPU faster than cloud GPU?
Usually, for the same silicon, because bare metal removes the layers cloud adds. On a single-tenant machine you get the whole GPU with no virtualization overhead, full NVLink and NVSwitch bandwidth between GPUs, and no noisy neighbours competing for host resources — and multi-node training can run over dedicated InfiniBand. Cloud GPU instances add a hypervisor, sometimes partition a physical GPU across tenants, and vary their interconnect by tier. For raw, consistent performance — especially large-model training or latency-sensitive inference — bare metal's undivided hardware tends to win. For many workloads the difference is modest; for the demanding ones it is real.
When is cloud GPU cheaper than bare metal?
When your GPUs sit idle much of the month. Cloud bills per hour (or per second), so if you run short experiments, bursty jobs, or occasional inference, you pay only for the time you use and nothing for the idle hours — which a flat monthly bare-metal rate charges you for regardless. Below a certain utilization, that pay-as-you-go model is genuinely cheaper, and it is the honest reason to choose cloud. The crossover is roughly a few hundred busy hours a month; below it cloud usually wins on cost, above it bare metal does.
What is the crossover point between them?
It is a utilization threshold rather than a fixed number, but the shape is reliable: a flat monthly bare-metal cost, divided by the cloud hourly rate for equivalent GPUs, gives the number of hours a month above which bare metal is cheaper. For high-end multi-GPU nodes that threshold commonly lands around a few hundred hours a month — well under a full month of continuous use. So any workload that keeps the GPUs busy most of the time, like sustained training or steady production inference, is usually past the crossover and cheaper on bare metal, before you even count cloud egress fees.
Do cloud egress fees really matter for GPU work?
Often more than people expect, because GPU workloads move large data. Training pulls big datasets in and writes large checkpoints out, and cloud providers bill data transfer out of their network per gigabyte. Move terabytes of results, sync checkpoints to another environment, or serve model outputs at volume, and egress becomes a meaningful line on the bill — one that a bare-metal server, where the data is local and there is no per-gigabyte egress meter, simply does not have. When you compare costs, the cloud hourly rate is only half the picture; egress is frequently the other half.
When should I use cloud GPU instead of bare metal?
When your workload is bursty, experimental, or genuinely needs elasticity. If you spin GPUs up for short training runs and down again, if your demand spikes unpredictably, if you are prototyping and do not want a commitment, or if you need to scale to many GPUs briefly and then release them, cloud's on-demand model fits that shape far better than fixed bare-metal capacity. We host bare metal, but we will tell you plainly: for spiky, low-utilization, or highly elastic work, cloud GPU is often the right and cheaper choice, and forcing it onto fixed hardware would waste money.
Can I get current GPUs like H100, H200, or B200 on bare metal?
Yes — bare metal is not a compromise on hardware. Current-generation accelerators, including H100, H200, and B200 class GPUs, are available as single-tenant bare-metal servers, so you get the latest silicon with the full performance and none of the virtualization layer. Which generation fits depends on your workload's memory and bandwidth needs rather than on bare-metal-versus-cloud, and it is worth choosing the GPU to the job. The point of bare metal here is that you are not trading away access to the newest hardware to get single-tenant performance and flat cost.
Does bare metal GPU mean I have to manage everything myself?
Not with us. You get a single-tenant GPU server with the hardware, the interconnect, and the network provisioned and operated, so you are not assembling infrastructure — you are running your workloads on dedicated hardware. The trade against cloud is fixed capacity versus elasticity and flat cost versus per-hour billing, not managed versus unmanaged. If your utilization suits bare metal, you get its cost and performance advantages without turning into a data-centre operator to claim them.
Sizing GPU infrastructure?
Tell us your workload and how many hours a month the GPUs will run. We will do the crossover maths with you, run bare metal when it fits, and tell you plainly when cloud's elasticity is the better call.