GPU servers for universities and research labs, on fixed budgets

GPU servers for universities and research labs work best when the cost is as predictable as the academic calendar. We rent dedicated NVIDIA and AMD GPUs by the month, at a flat price billed in advance, so a lab can match GPU spend to a grant line or a semester and stop paying when the work ends. Each server is assigned to your institution only, with root access, so you decide how students and researchers share it.

Budgeting GPU servers for universities and research labs

Monthly billing maps cleanly onto academic budgets. A 15-week semester spans four billing months, so one H100 for a course costs 4 × $2,190 = $8,760. A research group sharing an HGX H100 node for six months pays 6 × $16,900 = $101,400 before any term pricing, which you can ask for by ticket once your server is active, for periods of 6–12 months or for several servers. For the mechanics, see how billing works.

• The price is fixed for each paid month, and any change for future months comes with at least 30 days' notice.
• There is no setup fee and no egress fee within the included bandwidth: 20 TB a month on single-GPU plans and 50 TB on 8-GPU servers.
• Prices are in USD and exclude VAT and other taxes, which are added where applicable.
• Plans renew automatically from your account balance, with an email reminder before each renewal. Cancel from your dashboard before the renewal that follows the end of term and you stop paying; see renewals and cancellation.

GPU servers for teaching

For a course, size the GPU to the largest exercise, then decide how students share it:

• Intro deep learning with CNNs and small transformers runs well on an RTX 5090 with 32 GB or an L40S with 48 GB.
• LLM courses: a QLoRA exercise on a 7B–8B model needs roughly 10–20 GB including activations, so several students can share one 96 GB RTX PRO 6000 if jobs are scheduled. A 70B QLoRA project needs about 40 GB for the base model, which suits one H100 per team.
• Sharing: create Linux accounts, give each student a container, and assign GPUs with CUDA_VISIBLE_DEVICES or a scheduler. Jupyter and VS Code both work over SSH, as described in our remote development guide.

Whole GPUs are never sliced or shared with other customers, so the only contention is the one you manage among your own students.

Shared lab servers for research groups

An HGX H100 node gives a group eight 80 GB GPUs on NVLink, 2 TB of DDR5 and eight 3.84 TB NVMe drives. Installing Slurm on the node lets jobs queue fairly and request one to eight GPUs each. Groups training larger models can step up to an HGX H200 node with 1.13 TB of HBM3e. For simulations and FP64 workloads, our page on GPU servers for research and HPC compares the H100, H200 and MI355X.

You keep all rights to your data, and we access it only to operate or repair the service at your request, or when required by law. Local storage is not backed up unless agreed in writing, so keep copies on institutional storage, and check with your data office before uploading regulated or human-subject data. When a server is returned, its disks are securely erased.

An example course plan

Suppose a 40-student course runs weekly QLoRA labs on 8B models, then ends with team projects on a 70B model. At roughly 10–20 GB per job, one 96 GB RTX PRO 6000 holds about four to nine jobs at once, each running slower while they share the card, so two of them carry a lab section working in pairs with a simple queue. For the final month, ten teams of four each get one H100, where the 4-bit 70B base takes about 40 GB.

• Two RTX PRO 6000 servers for the four-month term: 2 × 4 × $1,390 = $11,120.
• Ten H100 servers for the final month only: 10 × $2,190 = $21,900, with volume pricing worth asking for by ticket at that quantity.

Cancel each server before its next renewal and the spend stops with the course. Because the price per month is known in advance, this plan can go into a budget request before the semester starts.

Procurement, invoices and payment

Ordering follows four steps in one form on the deployment page: you choose a configuration; set up the OS image, optional preinstalled software and your login (root password or SSH key); create a free account or log in; and pay the first month from your prepaid balance. The configuration and price are shown before anything is paid, which helps when a purchase needs internal approval, and once you pay, the server is delivered in under 10 minutes, with the IP and SSH details in your dashboard. The balance is in US dollars and is topped up in crypto: Bitcoin (BTC), Ethereum (ETH), Monero (XMR), Tether USDT on TRON (TRC-20) or Solana (SOL), from $100 to $10,000 per top-up, with +5% bonus credit for every full $100 in one top-up, up to +50% from $1,000; bonus credit is spent after paid credit, has no cash value and is not refundable. Card payments are not available yet, so check with your finance office that it can pay in crypto. Every server payment and renewal comes with an invoice, and every top-up with a receipt, in your dashboard; see invoices and VAT. For large orders we may ask for verification, such as institution details and the identity of the person ordering, and export control and sanctions rules apply to every order.

When a monthly server is the right fit

If your university's HPC center offers allocations and its queue fits your deadlines, use it. A monthly server makes sense when queue waits block a paper or a thesis, when you need root access or a GPU your cluster lacks, or when a course needs dedicated GPUs on fixed dates. For jobs that run only a few hours a month, hourly rental elsewhere will cost less.

To get started, deploy your servers before term: they are delivered in under 10 minutes after payment, and the first month starts on the day of delivery. Answers to questions about course or project needs are in our help center, docs and calculators.