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

# How does Kumo Engine scale?

> Understand demonstrated graph scale, context-generation performance, and hardware requirements

DiskGraph stores the graph index on local SSD and reads sampled neighborhoods for the model. This allows the graph to exceed system RAM without loading the full graph into GPU memory.

The published synthetic commerce benchmark processed **50 billion nodes and 175.5 billion edges** on one machine. Its **2.6 TiB index exceeded the machine's 1 TiB RAM**.

Generating context for one million seed entities took **5 seconds at two hops, 19 seconds at three hops, and 39 seconds at four hops** with a warm cache. These timings exclude index preparation and model training. They depend on graph structure, sampling, cache state, and hardware.

See [scale and model-input results](/kumo-engine/resources/benchmarks#scale-and-model-input-performance) for hardware details, deeper neighborhoods, and cold-cache measurements. For build time and resource use, see [graph materialization](/faq/kumo-engine/data-graphs/how-long-does-graph-materialization-take). The tested graph size is a demonstrated workload, not a capacity ceiling.