> This page is for Self-Hosted, version v3.0 (default).
> For other versions, use one of these documentation indexes:
> - v3.0 (default): https://docs.nvidia.com/run-ai/self-hosted/v3.0/llms.txt
> - v2.26: https://docs.nvidia.com/run-ai/self-hosted/v2.26/llms.txt
> - v2.25: https://docs.nvidia.com/run-ai/self-hosted/v2.25/llms.txt
> - v2.24: https://docs.nvidia.com/run-ai/self-hosted/v2.24/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/run-ai/_mcp/server.

# Network Topologies

The Network Topologies API enables administrators to reflect the hierarchical network topology connectivity of nodes in a data center, such as racks, blocks, and other organizational units, to improve pod scheduling decisions for communication-intensive AI/ML workloads. To support topology-aware scheduling, this API allows administrators to define a multi-level network topology element using ordered Kubernetes node labels that represent the network connectivity of nodes in the data center. Nodes are labeled with the same key-value pairs labels that represent their location in the data center network. These labels are used by the NVIDIA Run:ai Scheduler to prefer scheduling pods on nodes that are "closer" to each other, minimizing communication overhead and optimizing workload performance.