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Launch self-serve GPU compute for demanding work.
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Scientific modeling with Compute
Run simulations when you need them, without waiting for capacity
Launch GPU instances for parameter sweeps, simulations, post-processing, and long runs, then pay only for the time you use.
Why teams choose Compute for scientific work
On-demand clusters
Move from 1× to 8× GPUs in minutes for sweeps, ablations, and longer runs.
Jupyter-friendly workflows
Explore, visualize, and iterate from notebooks when that fits the way your team works.
Straightforward pricing
Use per-second billing with no separate egress charges and pricing that already includes compute, storage, and data transfer.
Keep data close to the workload
Keep inputs and outputs near the nodes when latency, throughput, or governance matter.
Common scientific workloads
Computational physics
Particle systems, finite-element and finite-volume solvers, and Monte Carlo studies.
Molecular workloads
Docking, coarse-grained dynamics, GPU-accelerated scoring, and sampling.
Earth and climate models
Downscaling, ensemble analysis, post-processing, and visualization.
Data processing
Large raster stacks, point clouds, and matrix-heavy jobs that benefit from GPU math.
How it works
Swipe left to see more
Launch an Ubuntu or PyTorch image on a 4090 or 5090 instance.
Open Jupyter or work directly through your CLI.
Bring your data into the instance, run your solver, and export the results you need.
Turn a working environment into a custom template for repeatable runs.
Patterns teams use most
Parameter sweeps on 4090
Heavier single-node compute on 5090
Post-processing and visualization in notebooks
Pricing at a glance
We offer on-demand for time-critical runs. Billing is per second and all-inclusive.
RTX 5090
1 × - 8 ×
vCPU - - -
RAM - - - GB
Disk space - - - GB
Bandwidth - Mb/s
RTX 4090
1 × - 8 ×
vCPU - - -
RAM - - - GB
Disk space - - - GB
Bandwidth - Mb/s
Start self-serve, go deeper when needed
Try Compute directly if you want to test setup, pricing, and workflow fit for yourself. Talk to sales if your team needs rollout planning, larger deployments, support options, or a more structured path into production work. The docs are there when you want to move faster.
Questions teams usually ask
