Compute
Compute overview
Compute planning in SAVRN Cloud starts with the workload and the service offered to a project. This guide explains how supplier placement, resource limits and operating responsibilities fit behind that offer, using simulated allocation records rather than claims of available GPU stock.
SAVRN Cloud · Coming soon · Public preview · Browser-local simulation · No live compute or payments
Explore the proposed supply layer
Open Compute, filter provider cards by region or source, then choose Configure to open the deployment form. Review model, GPU/region, hour ceiling, context, concurrency and route settings. Choose Review allocation, acknowledge the simulation boundary and choose Create local deployment. Inspect the resource in Deployments.
These screens show the proposed SAVRN Cloud allocation workflow. They do not query supplier stock, request quota, create a virtual machine or verify a GPU. Every displayed resource and rate is a fixture unless explicitly linked as external evidence.
Keep customer and supplier records separate
A customer chooses an eligible SAVRN Cloud service offer. Operators choose and qualify the supplier configuration behind that offer. The customer-facing record should preserve a stable project and request history when placement changes. Provider-specific lifecycle details belong behind an explicit adapter.
Provider names identify illustrative placement choices, not a supplier commitment or confirmed capacity. Any future rented or owned placement requires its own qualification and published service terms.
Before connected services launch
Start with one supported configuration and a bounded operating window. Verify hardware, image, model, private access, resource lifetime, cleanup and actual cost. On-demand allocation, reserved capacity, multinode jobs and a marketplace are separate capabilities. Do not infer them from a compute catalog. Use rented execution and owned transition to review their distinct acceptance requirements.
Build toward the work that matters.
Tell SAVRN what your institution needs to run, who reviews the results and where its data must stay. That workload defines the next service to qualify.
Discuss an AI project