01
Nobody knows who’s on the GPU
Someone kicked off a long job, someone else jumped the line. The machine is busy and nobody knows for how much longer.
AI research teams
GPUs should run experiments, not queues
Everyone knows when resources free up, where experiments run, and where results came from. Multiple projects move forward at once.
GPUs shared across the teamIsolated environments per projectBurst capacity for peaks
The blockers
Even with hardware, teams can spend every day waiting for machines and configuring environments.
02
Switch machines, break everything
CUDA, drivers, and framework versions don’t line up. Half a day gone before the first experiment runs.
03
That one result is nowhere to be found
Parameters, data, logs, and checkpoints live apart. Weeks later, piecing the run back together is its own research project.
04
Short at peak, idle the rest of the time
Buying for peak is expensive; relying on fixed hardware means long queues when deadlines loom.
How we solve them
Jobs, environments, and records in one clear workflow.
Jobs queue themselves
Members submit jobs; GPUs get assigned by policy.
No more asking around about who’s using what, or for how long.
Environments stay isolated
Different frameworks and versions run side by side without stepping on each other.
Fewer surprises when you switch machines.
Logs and usage stay on record
Failed jobs can be traced back, and leads can see where capacity went.
Every experiment has an audit trail.
Expand when the peak arrives
Run everyday experiments locally; pull in cloud capacity for concentrated training pushes.
No idle hardware kept alive for a few peak weeks.
Deployment options
There is no single right answer. Where your data lives, how long jobs run, and what hardware you already have all shape the choice.
01
Cloud
Validating a new direction, a short intensive training push, or hardware still on order.
Fast to start, and resources flex with the workload.
02
On-premise
Long-running experiments, internal data, and teams that already own GPUs.
Data and environments stay in-house.
03
Hybrid
Develop and run small experiments locally; send the big jobs to the cloud.
The keys are identical environments and data that moves smoothly.
How it comes together
One entry point for resources, a record of every run, and capacity released when the job ends.
- 01
Pick the environment
Choose the framework, dependencies, and data for the project.
- 02
Submit the job
Specify GPU count and expected runtime.
- 03
Track progress
Follow the queue, logs, and resource usage.
- 04
Save the results
Keep the model and logs, then release the resources.
Tell us about your workload
Tell us your team size, GPU configuration, and the jobs you run most — then we’ll judge whether more hardware is even needed.
No need to pick a GPU model first
Existing code can go for a test run
FAQ
Can different projects use different CUDA and framework versions?
Yes — environments are kept separate. Before launch, we validate against your existing GPUs, drivers, and code.
Can we cap how many GPUs one person takes?
Yes — limits can be set per user, project, or job, with usage logged.
Can cloud and on-prem GPUs work together?
Yes. First decide which data can go to the cloud, then unify environments and the job entry point.
Do we have to change our training code?
Usually not. Run an existing job first, then adjust data paths and launch scripts only if needed.