NVIDIA's new Kumo model family provides a knowledge layer for structured data, completing the data plumbing alongside the Nemotron retriever series.
On October 21, 2026, at GTC Berlin, NVIDIA announced the Kumo family of foundation models, designed specifically to connect AI agents to structured enterprise data. Part of the broader Agent Knowledge Foundation, Kumo addresses the ‘Data Plumbing’ challenge identified in the TGVP infrastructure report, where the inability to reason over databases remains a primary barrier to autonomous agent deployment.
The Kumo family includes specialized models for Tabular, Relational, Time Series, and Forecast data. While the company’s Nemotron series handles unstructured documents, Kumo provides the prediction layer over structured records. In the announced workflow, the Nemotron tools parse documents, embed queries and rerank information, while the new Kumo models make predictions from structured data – classification and regression, in the model card’s terms. As CEO Jensen Huang framed it at the keynote: “Ultimately, what turns these super agents, these agents into super agents, is that you give them access to your company’s knowledge.”
Availability is immediate: Kumo models are available as open weights on Hugging Face and packaged as NVIDIA NIM microservices on build.nvidia.com for production inference. The integration into the open-source NVIDIA Agent Toolkit allows developers to combine Kumo with Nemotron Retriever and OpenShell enforcement, creating a full-stack governance and knowledge foundation. NVIDIA claims the Kumo Tabular model provides up to a 20% improvement in prediction accuracy over standard gradient-boosted decision trees for agentic workloads (company-reported).
For enterprise architects, Kumo represents the verticalization of the data layer. By building a dedicated moat around how agents access structured data, NVIDIA is positioning itself as the essential infrastructure provider for both the safety (OpenShell) and the knowledge (Kumo) of the agentic economy. This release suggests that the future of agentic RAG is moving beyond simple document retrieval and toward a unified framework that treats structured databases as first-class citizens in the agent’s context.
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