GPU capacity on the term, price and terms that fit your workload
Need GPUs but don't want to buy or host the hardware? We compare leasing options across cloud providers, GPU specialists, data centres and hardware finance, then bring you a shortlist with what is confirmed and what isn't.
GPU leasing means paying for the use of GPU compute for a set period instead of buying the hardware outright. The term can be a few hours on a cloud instance or several years on a dedicated cluster built for you. Who owns the hardware, where it runs and who looks after it all depend on the type of lease.
Leasing suits teams whose demand is uncertain or growing fast, projects with a fixed end date, and organisations that would rather spend from operating budgets than tie up capital in hardware that the next GPU generation will overtake. It also lets you start this month, without waiting for a server build, a power upgrade or space in a data centre.
Buying usually wins when your GPUs will be busy most of the time for three years or more, when you already have the space, power and cooling, or when data must stay on hardware you own. Many organisations do both: they own a steady base of capacity and lease extra for training runs, launches and peaks.
The main ways to lease GPU capacity, side by side. Terms and commitments vary by provider; these are the usual patterns.
| Option | Typical term | Where it runs | Hardware owned by | Commitment | الأنسب لـ |
|---|---|---|---|---|---|
| On-demand cloud GPUs | Hours to weeks | Provider's cloud | المزود | None: pay as you go | Experiments, proofs of concept, unpredictable short jobs |
| Spot and burst capacity | Minutes to days | Provider's cloud | المزود | None, but can be interrupted | Fault-tolerant batch jobs, hyperparameter sweeps |
| Reserved cloud capacity | 1 to 36 months | Provider's cloud | المزود | Fixed term, often part paid upfront | Steady training and inference at a lower unit price |
| Dedicated bare-metal servers | 1 to 36 months | Provider's data centre | المزود | Fixed term per server | Full control of the machine, predictable performance |
| Private cluster lease | 6 months to 5 years | Provider's or a third-party data centre | Provider or lessor | Contract for the whole cluster | Large training runs, foundation models, multi-node jobs |
| Fractional and shared GPUs | Hours to months | Provider's cloud or your platform | Provider or you | Low | Inference, notebooks, teaching and small models |
| Hardware finance lease | 2 to 5 years | Your premises or colocation | Lessor during the term | Fixed monthly payments | Keeping data on your own site while spreading the cost |
| Lease to own (hire purchase) | 2 to 5 years | Your premises or colocation | You, at the end of the term | Fixed monthly payments | Owning the hardware without paying for it all upfront |
Start a GPU instance when you need it and stop paying when you shut it down. It's the quickest way to get going, and the most expensive per GPU hour if you run for long.
Typical set-up: Single GPUs up to 8-GPU instances (for example H100, H200 or L40S), billed by the hour or second, with storage and networking billed separately.
Spare capacity sold at a discount, which the provider can reclaim at short notice. It's very good value if your jobs checkpoint often and can restart.
Typical set-up: Discounted instances on the main clouds and GPU specialists, used alongside on-demand or reserved capacity.
Commit to a set number of GPUs for a fixed term in return for a lower hourly rate and guaranteed access. This is how most teams run steady training and production inference in the cloud.
Typical set-up: Blocks of 8 to several hundred GPUs reserved for 1 to 36 months, sometimes with a start date months ahead for capacity still being built.
Whole GPU servers dedicated to you, with no virtualisation layer and no other tenants. You choose the operating system, drivers and software stack.
Typical set-up: 8-GPU HGX servers (H100, H200, B200 or B300) or PCIe servers with RTX PRO 6000 or L40S GPUs, leased per server per month.
A complete multi-node cluster reserved for you: GPU servers, high-speed fabric, storage and scheduler. It's built, or set aside, for your workload and leased for the whole term.
Typical set-up: 32 to thousands of GPUs (HGX B200 or B300 nodes, or GB200 / GB300 NVL72 racks) with InfiniBand or high-speed Ethernet, leased for 6 months to 5 years.
Part of a GPU instead of a whole one. Multi-Instance GPU (MIG) splits one data-centre GPU into up to seven isolated slices, and virtual GPU software shares a GPU between virtual machines.
Typical set-up: MIG slices on H100, H200 or B200, or vGPU profiles on RTX PRO and L40S GPUs, billed by the hour or month.
The GPU servers are installed at your premises or in a colocation facility you choose, but a finance company owns them and you pay monthly. At the end you return them, upgrade, or carry on at a lower rent.
Typical set-up: AI servers or workstations on a 2 to 5 year lease, installed and supported by us, with colocation arranged if you need it.
Monthly payments over a fixed term, after which the hardware is yours. It costs more in total than paying upfront but keeps cash free for the project.
Typical set-up: AI servers or a DGX-class system bought through hire purchase over 2 to 5 years, with installation and support from us.
Start from the workload, not the GPU. These questions usually narrow the options to one or two.
Days or weeks: on-demand or spot. Months: reserved capacity or bare metal. Years: a private cluster, a finance lease or lease to own.
If the GPUs will be idle much of the time, pay per hour. Once they're busy most of the day, a fixed-term lease normally costs less per GPU hour.
Multi-node training needs a high-speed fabric such as InfiniBand and GPUs in the same cluster. That points to a private cluster or a reserved block, not scattered instances.
If data must stay in the UK, the EU or on your own premises, filter by region first. A finance lease or colocation keeps the hardware where you choose.
Cloud and managed options include the platform. Bare metal and leased hardware need a team to operate them, which we can provide.
Operating budget: cloud, reserved or bare metal. Spread capital spend: a finance lease. Own it at the end: lease to own.
The more of this we have, the more precise the shortlist. Rough answers are fine to start with.
For example 64 × H200, 8 × B200 or 4 × RTX PRO 6000. Say if another GPU would also do.
How long you need the capacity, when it must be ready, and whether you may need to extend.
Single-node or multi-node, InfiniBand or Ethernet, and how much fast storage the dataset needs.
Countries or regions the data can and can't leave, and any certifications you need (ISO 27001, Cyber Essentials Plus, SOC 2).
A target price per GPU hour or per month, and whether upfront payment or a deposit is acceptable.
Whether you want bare capacity, a managed platform, or us to run the cluster for you.
We agree the workload, GPUs, term, region and budget with you, and what you're prepared to trade off.
We compare clouds, GPU specialists, data centres and finance partners for capacity that matches.
Each option lists the GPU model, term, price basis as the provider states it, location, and whether the capacity is running today or still planned.
We confirm availability and price with the provider before you decide. Nothing is assumed from a price list.
We help you check the contract, then set up access, networking, storage and your software stack.
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Send us the GPUs, term and region and we'll come back with a shortlist of options, each with what's confirmed and what isn't.
Ask about GPU leasing