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GPU cloud for life sciences and drug discovery
This page is a beginner guide for people in drug research and biology who need extra computers for a project. A GPU is a graphics processing unit: a chip that is very good at doing many small math steps at the same time. GPU cloud means you rent those chips by the hour from a company, then turn them off when the work ends.
Who it is for: chemists, computational biologists, and the people who buy compute for a lab. You do not need to be an engineer to follow this page. You do need a burst of work that a laptop cannot finish.
You rent GPUs when you want to test a large library of candidate molecules this month, teach a computer model about proteins, rebuild 3D shapes from a special microscope, or simulate how atoms move. You do not buy a room of servers for a campaign that may last six weeks.
If those same machines stay busy for years, the next problem is usually electricity and a site, not another rental. That is powered land: land with a credible path to large-load power. This page is about buying compute. It is not medical advice.
You rent GPUs when a screening campaign is a burst
Virtual screening means using a computer to score a huge list of candidate drugs against a target, then throwing most of them away. That job comes in waves. You expand a library, run the scores, pick a short list, and go quiet until the next design cycle.
That is a classic rental. Elastic capacity (machines you can add and remove) beats a lab full of idle chips. Ask whether the provider can give you enough matching GPUs for the window, save progress so a crash does not wipe the run, and let you shut the meter off when the screen ends.
If the campaign is one-off, rent. If screens run every week and the queue never drains, you are already describing a cluster you might need to own or house.
You rent GPUs when a protein model will not fit on a laptop
A protein language model is a computer program that learns patterns in protein sequences the way a chatbot learns patterns in words. Training one (teaching it from lots of examples) is not desk work. It wants several GPUs in one machine, a lot of memory on each GPU, and a place to save checkpoints. A checkpoint is a snapshot of the model so you can restart if something fails.
NVIDIA publishes BioNeMo, a software framework for building and adapting models of proteins and small molecules on NVIDIA GPUs. CUDA is NVIDIA's software layer that lets those programs use NVIDIA chips. You do not have to use BioNeMo. You do need a cloud image that can run CUDA software. A short fine-tune (a smaller teaching job on top of an existing model) can still be a rental. Training a large model from scratch, or keeping an internal chemistry assistant on all day, is how the machines stop looking temporary.
You rent GPUs when a microscope dump has to become a 3D shape
Cryo-EM is cryogenic electron microscopy: a way to take many 2D pictures of frozen molecules and rebuild a 3D shape. The instrument writes large files. The rebuild wants a GPU with enough of its own memory, plus fast local disk (scratch space next to the chip, not a slow copy from another country).
Cloud rental works when a core facility or a contract research shop spikes one dataset through a pipeline and then idles. It fails when every microscope night lands on the same machines and the data never leaves the building.
Data gravity shows up here first. Data gravity means the files are so large that moving them costs more time and money than the compute. Keep the computers near the data, or keep the data on a network you control.
You rent GPUs when sequencing or molecular dynamics is a campaign, not a factory
Genomics here means reading DNA or RNA. Single-cell work means doing that for many cells at once. These pipelines mix ordinary CPUs (general computer chips) with sharp GPU peaks. NVIDIA Clara is a set of healthcare and life-sciences developer tools. Inside that family, NVIDIA documents Parabricks as a GPU path for sequencing workflows. Rent to clear a backlog. A genome service that runs every day is a cluster, not a weekend of extra cards.
Molecular dynamics means simulating how atoms jiggle and bind over time. GROMACS is a widely used free program for that job. Its install guide treats NVIDIA GPUs as a CUDA build: it can use NVIDIA chips if the cloud machine has the right NVIDIA software. Short campaigns belong on rental. Long runs that occupy the same machines for months are how a "temporary" cluster becomes a site. If the job spans several machines, ask about MPI (software that splits one job across servers) and whether it can talk to the GPUs directly.
Physics, climate, and airflow simulations live on GPU cloud for physics, climate, and CFD.
What to check before you rent
Start with the software, not the famous product name. Confirm NVIDIA drivers, the CUDA toolkit version, and that BioNeMo-class or GROMACS CUDA builds will actually run. Several chips in one server is a different product from a single card in a notebook.
If the work is campaign-shaped, rent. If the machines are busy unless someone is on vacation, you are paying a retail meter for work that never turns off. Ask where the data lives. Research datasets still have residency, contract, and export rules. Demand a written story for several GPUs at once: how they connect, where checkpoints go, and what happens if one machine dies.
GPU cloud partners (placeholder)
Partner rows below are placeholders until terms are signed. No live rates. No outbound URLs. When links go live, they will use rel="sponsored".
| Best for | GPU types | Notes |
|---|---|---|
| Partner TBD - burst docking and virtual screening | NVIDIA data-center GPUs | Placeholder. Confirm CUDA software and whether the job can be paused. rel="sponsored" when live. |
| Partner TBD - protein model training and fine-tunes | Multi-GPU NVIDIA nodes | Placeholder. Ask how GPUs connect, how much memory they have, and where checkpoints go. rel="sponsored" when live. |
| Partner TBD - GROMACS-class molecular dynamics | NVIDIA GPUs with CUDA builds | Placeholder. Confirm toolkit version and multi-machine support. rel="sponsored" when live. |
This table is not a ranking. It will reflect partner relationships once partners exist.
GPU cloud pricing varies. Verify live rates.
There is no honest single dollar-per-hour number on this page. List prices move with GPU type, region, and whether you pay only when the machine is on. Quoted "from" rates often skip storage, data copies, and idle reservations. See the GPU cloud pricing index for how we talk about rates. We will not invent hourly prices here. Treat every public figure as stale until you re-check the provider. Compare hours you will actually run. If the cluster is busy for years, rental is a cash-flow choice, not a bargain by default.
Research data is not the same as clinical data
Most drug-discovery GPU work is research: structures, assays, simulation files, sequencing used for target biology. That is still sensitive. It is not automatically clinical care data. If a workflow crosses into protected health information (health data that can identify a person), stop and use GPU cloud for medical imaging and clinical NLP. This page does not certify HIPAA, GxP, or any other regime. Keep working files in the same region as the GPUs. Repeated downloads of large microscope movies are how a cheap GPU becomes an expensive pipeline.
When GPU rental stops working
Rental stops working when the machines stay busy on a calendar you can plan in years, the hardware mix is stable, and the next constraint is megawatts (a large unit of electrical power) and a site, not another login. You are then running a cluster on someone else's floor. The alternative is powered land: a development site with a credible path to large-load electricity. Browse the U.S. data center directory, then read when GPU clusters need powered land. Hybrid is normal. Rent the bursts. Site the work that never turns off.
Find powered land when you need a site. Rent GPUs when you need capacity now.
Related
- GPU cloud for medical imaging and clinical NLP
- GPU cloud for physics, climate, and CFD
- When GPU clusters need powered land
FAQ
Do life-sciences teams need GPU cloud or a local workstation?
A workstation on a desk is fine for methods, pictures, and small systems. Screening campaigns, protein-model training, and production molecular dynamics outgrow a desk. Rent GPU cloud for those bursts unless you already own a busy cluster.
What is BioNeMo, and does it mean I should rent?
BioNeMo is NVIDIA software for biomolecular models on GPUs. It is not a hosting decision. Rent if you are still changing model size and how often you run. Buy or site hardware when the same jobs occupy nodes year-round.
Can I run GROMACS on rented NVIDIA GPUs?
Yes, if the cloud image has a CUDA toolkit the GROMACS build accepts. For jobs that span several machines, the message-passing software also has to talk to the GPUs. Confirm that with the provider before you move a production system.
When is a dedicated cluster cheaper than GPU rental?
When utilization stays high for a planning horizon measured in years, not sprints, and you can use the same GPU mix the whole time. Run your own hours and quotes. This page does not publish rates. Start from the pricing index, then re-check the provider.
What is the alternative when rental queues and power become the limit?
Look at powered land and the data center directory, and use the powered-land GPU guide. The product you need has shifted from cards to a site with power.
Sources
- NVIDIA BioNeMo Framework
- NVIDIA Clara
- GROMACS installation guide
- What is powered land
- U.S. data center directory
- Powered Lands data center map
Need land or power instead? See the map or contact.