No quotas
spin up a GPU, no approvals
Per-second
pay for compute, not queue time
InfiniBand
multi-node VM Cluster
Trusted by researchers at leading universities
From the research community
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
“Special thanks to OpenRouter and VESSL AI for their support in evaluation and training.”
optimize_anything: A Universal API for Optimizing any Text Parameter
“This research is supported in part by gifts from … and VESSL AI.”
Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants
“This work was supported in part by the Google Research Scholar Program and by compute resources from the Center for Human-Compatible AI (CHAI) and VESSL AI.”
Graph-Based Alternatives to LLMs for Human Simulation
“This work was supported by compute resources from VESSL AI, the Center for Human-Compatible AI at Berkeley, and the BAIR-Google Commons program.”
SAE-RNA: A Sparse Autoencoder Model for Interpreting RNA Language Model Representations
“This work was made possible by the VESSL AI GPU platform, with the support from A100 GPUs and external storage.”
The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models
“We thank VESSL AI for supporting this research with GPU compute resources.”
Built around the research cycle
Prototype alone, sweep as a team before a deadline, then scale up for the result that goes in the paper. Most of what slows that cycle down is infrastructure, not the idea.
Prototype on a single GPU
Early-stage research is mostly iteration: try an idea, watch it fail, change one thing, run it again. A single self-serve GPU billed by the second means testing an idea costs minutes, not a quota request.
Workspaces stay interactive: connect VS Code or a notebook, pause between sessions, and keep the environment without paying for an idle GPU.
$1.39/hr
A100 SXM on-demand, self-serve
Per-second
billing, so a quick test costs what it should
Pause, don't pay
keep a workspace's state without the GPU running
Sweep before the deadline
The week before a submission looks nothing like the months before it: dozens of runs across seeds, architectures, and hyperparameters, launched at once and left to report back.
Fan a sweep out from the CLI, pay only for the GPU-seconds those runs actually use, and release everything the moment results are in.
One command
vesslctl fans a sweep out across the GPUs you need
No commitments
spin up for an afternoon or a weekend push
Per-second
billing matches bursty, deadline-driven demand
Scale up for the camera-ready run
The result that goes in the paper is usually bigger than anything tested during the sweep, and that's when a single node stops being enough.
A VM Cluster hands you multiple GPU nodes wired with InfiniBand and root access, so the final run scales without a new procurement cycle.
8 per node
H100 to Blackwell, 1–8 nodes
InfiniBand
between nodes, on by default
Root SSH
on every node, bring your own scheduler or stack
It's rarely the GPU that's missing. It's the week lost to a queue, a quota request, or an environment that won't build.
Research doesn't run on a steady schedule
Academic compute needs move in bursts, around deadlines, courses, and whoever's turn it is on the cluster.
Deadline-driven bursts
A conference or grant deadline turns a quiet lab into dozens of parallel jobs overnight: sweeps across seeds, architectures, and hyperparameters, then quiet again while results get written up.
Small teams, big tool sprawl
A single PhD student ends up juggling experiment tracking, model hubs, containers, and version control, often logging runs by hand in a spreadsheet because nothing else was set up for them.
Shared, contended infrastructure
Campus clusters and lab GPUs are split across students, courses, and projects. One long-running job can quietly block everyone else's next experiment.
Every project starts from zero
New students and new papers both start the same way: reinstalling CUDA, chasing driver mismatches, and rebuilding an environment before any research actually happens.
Where infrastructure eats into research time
The blockers researchers describe are rarely about the price of a GPU-hour.
The queue
Shared clusters can mean days of waiting before a job even starts, right when a deadline makes every day count.
Setup that burns the budget
Environment setup and data migration can quietly consume as much of a compute budget as the training run itself.
No visibility into who's using what
One idle GPU held open, or a course using far more than its share, is hard to see and harder to fix without a dedicated admin.
An interface built for infra engineers, not researchers
Command-line-only clusters are a real barrier for students who need to focus on the model. A notebook or web UI shouldn't be an afterthought.
How VESSL Cloud fits
Compute plus workflow, mapped to how research actually gets done.
Self-serve GPUs, no quota ticket
Pick up a GPU the moment a sweep starts, billed by the second with no quota ticket or approval step in the loop. Release it the moment you're done.
Reproducible environments
Pin your CUDA, framework, and library versions into a container image once. Every job and workspace starts from that exact setup, so a labmate can rerun your experiment without rebuilding it.
Workspaces for interactive work
Connect a notebook, VS Code, or SSH to a persistent GPU workspace, with no CLI-only barrier for students who'd rather focus on the model than the infrastructure.
Scale up for the big run
When a recipe is ready for the full-scale training behind a paper's headline result, move to a multi-node VM Cluster with InfiniBand and root access.
Storage that doesn't force a choice
Keep datasets on lower-cost Object Storage between sweeps, and share files across concurrent jobs with fast Cluster Storage. Attach either to any job.
Academia on VESSL Cloud: FAQ
What GPUs are available for academic research?
A100 and H100 are self-serve starting at $1.39/hr and $3.19/hr, with no quota ticket to file. H200 and Blackwell GPUs (B200, B300, GB300) are available on request. For larger labs and grant-funded projects, you can reserve capacity ahead to line up with your timeline.
Do I need a long-term contract or grant approval to start?
No. On-demand GPUs are billed per second with no commitment, so you can start on a personal card or a small grant. When usage becomes steady, reserved capacity starts at three-month terms with up to 15% off on-demand pricing.
Can multiple lab members share one environment?
Yes. Pin your setup into a container image once, and every teammate's job or workspace starts from the same environment, with no more version mismatches between labmates.
How do I move datasets between experiments?
Create an Object Storage volume for lower-cost archival between runs, or use Cluster Storage for fast, concurrent access across jobs. Attach either to any job or workspace at runtime.
Where AI models
get their GPUs.
- Start in minutes
- Scale to multi-node clusters
- Capacity reserved to your timeline
- Dedicated support