Private GPU pools and reserved GPU capacity, explained

Reserved GPU capacity, or a private GPU pool, provides dedicated Runpod GPU resources for organizations that have predictable baseline demand and need supply assurance, while on-demand GPUs remain suited to experiments, irregular workloads, and uncertain peaks. Customers pay for reserved capacity throughout the commitment term regardless of utilization, so sizing should be based on consistently used capacity, supported by historical usage, expected growth, GPU type and region requirements, and the business impact of unavailable compute. Reserved pools can serve Pods, Serverless endpoints, and Clusters within Runpod’s managed container environment, but they do not provide bare-metal access. When enabled, bursting allows workloads that exceed the reserved pool to use separately billed on-demand capacity, although teams should confirm available caps, queuing, and hard-stop controls based on workload needs. The guidance recommends reserving the predictable floor of demand, sharing pools across compatible workloads where possible, using on-demand capacity for variable demand, and reviewing utilization and renewal plans before a commitment expires.

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