Computing capacity for engineering simulation, AI model training, and repeated design studies. Graphics processing units (GPUs) work on many calculations at once.
A GPU die is a processing chip; a node is a compute server. These figures describe KYAMOS' stated installed capacity, not the number currently reporting or available to reserve. Contact us to confirm the equipment and capacity suitable for your workload.
A compact view of the equipment currently reporting measurements. The sample may cover only part of our capacity; individual server details remain private.
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This is a partial sample, not an inventory or a booking guarantee. It is separate from the company-stated capacity above. GiB means gibibytes, a unit of memory. Individual servers and internal connection details are not published.
Discuss the physics, model size, and checks required for your engineering study. We can assess which computing workflow fits the problem.
Train models on simulation results and evaluate fast AI predictions. Accuracy and speed depend on the model, training data, and test case.
Explore several design choices or operating conditions. Agree the workload, evidence required, and available capacity before starting a project.
For engineering and AI teams needing computing capacity. Tell us about your software, model size, memory needs and expected number of runs.
Pricing: quoted per project. Scope, computing needs, validation checks and support determine the quote. Contact us to agree access and deliverables before work starts.
Capacity and supported software are confirmed for each project; the public resource summary is not a reservation or availability guarantee.