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You can make tabular predictions without fitting a new model by using NVIDIA Structured Data Models (SDM) for in-context learning. Supply labeled context rows and unlabeled query rows with the same feature columns; the model predicts the query targets in a forward pass. No dataset-specific gradient updates or hyperparameter tuning are performed, but you still need labeled examples, correct semantic types, leakage-safe splits, and an evaluation plan.
What “zero-training” means in SDM
Traditional tabular machine learning fits parameters to each dataset. NVIDIA’s structured-data models instead treat your labeled rows as context. A predictive request contains the feature values and known targets for the context rows, plus feature values without targets for the query rows. The model infers the query targets in one pass.
This is zero-training at the dataset level: there is no task-specific parameter fitting, gradient update, or hyperparameter search. It does not mean you can omit labels, schema definitions, task framing, or validation. A classification request still needs a target column whose semantic type is categorical, and a regression request needs a numeric target.
Choose the model that matches your data
| Model | Data shape | Tasks and outputs | Typical execution | Important qualification |
|---|---|---|---|---|
| KumoTabular | One table | Classification returns class probabilities; regression returns numeric predictions | Local SDM library or hosted Kumo Tabular NIM | Use when all predictive features are in a single table |
| TabICLv2 | One in-context table | Classification and regression for held-out rows | Local SDM library | Labeled rows are supplied as context for the rows being predicted |
| KumoRelational | Multiple related tables represented as a graph | Relational predictive queries | Relational client and NIM workflow | Choose this when relationships between tables are part of the signal |
| TabFM | Tabular data | Confirm supported task types for the version you deploy | Structured Data Models library | Check the release’s support and licensing terms before deployment |
Do not infer latency, maximum context size, accuracy, or licensing from the model names. Those properties are version- and deployment-specific and should be checked in the documentation for the exact library or NIM image you will run.
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Requirements for a local run
- Install the open-source
structured-data-modelspackage for local execution. The library documentation lists Python 3.11 or newer and PyTorch 2.7 or newer. - Use
structured-data-models-clientwhen your application will call a running NIM endpoint instead of loading the model locally. - Have a pandas table with a clearly identified target and feature columns. Convert it to
sdm.TableTensorand infer or explicitly set semantic types. - For the documented GPU workflow, select a CUDA device that is supported by your installed PyTorch and model build.
- Keep enough labeled context rows to represent the cases your query rows contain. Zero-training removes fitting, not the need for informative examples.
Local prediction workflow
-
Install the package and verify the runtime
Install
structured-data-modelsin an environment meeting the documented Python 3.11+ and PyTorch 2.7+ requirements. Confirm that PyTorch can see the CUDA device if you intend to run on a GPU. -
Load and type the table
Read the data into pandas, then convert the frame to an
sdm.TableTensor. Preserve the target separately when you define the feature schema. Set semantic types explicitly when inference could be ambiguous; in particular, mark a classification target as categorical rather than numeric.Rank #2
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Separate context and query rows
Create
x_contextandy_contextfrom rows whose targets are known. Createx_queryfrom rows for which you want predictions. The context and query feature columns must match in meaning and order, even when their row counts differ.What’s actually slowing this PC down?
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Instantiate an in-context model
For a single-table task, instantiate
sdm.models.TabICLv2orKumoTabular, selecting the CUDA device in the documented GPU configuration. Use KumoRelational through the relational workflow when the question spans related tables.Rank #3
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Run the forward pass
Pass the labeled context and unlabeled query tensors to the model’s in-context prediction interface. KumoTabular produces class probabilities for classification and numeric values for regression. The returned predictions are
TableTensorobjects. -
Convert and reuse results
Convert the result to pandas, Arrow, or cuDF for downstream work. If you issue repeated queries against the same context, use the documented
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Calling a hosted Kumo NIM
A hosted deployment keeps model serving behind an NVIDIA NIM endpoint. The client workflow is different from constructing local tensors but follows the same context-and-query idea.
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structured-data-models-clientand configure the NIM URL for your environment. - Create a
PredictiveQuerydescribing the table or relational schema, target, features, and prediction request. - Materialize the indices requested by the predictive query so the client has the context and query rows needed for inference.
- Call
SDMClient.predict(request, model=KumoTabular())for a single-table request, or useKumoRelational()for a relational request. - Validate the returned schema and prediction type before writing results to production systems. NIM availability, model versions, parameter ranges, and license terms can change, so check the deployed release rather than relying on an older installation.
Prevent temporal and target leakage
Static predictive questions
For static queries, the current documentation uses an 80/10/10 split by default. This is a documented data-splitting default, not an accuracy result. Keep the target out of query features and make sure context rows do not contain information that would only become available after the prediction would be made.
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Temporal predictive questions
Temporal requests require ordered, non-overlapping splits. A prediction at time t may use only rows and aggregates available before t. Define the anchor time and every aggregation horizon explicitly. If a window extends into the future relative to the anchor, the model receives leaked information and the evaluation will be misleading.
How to evaluate a zero-training prediction
Zero-training does not eliminate evaluation. Hold out rows that reflect the way predictions will be consumed, run the same request structure you will use in production, and inspect both the prediction output and its schema. For classification, examine class-probability behavior with metrics appropriate to your class balance and decision threshold. For regression, select an error metric that matches the cost of the business decision. The official material does not establish one universal accuracy number for this workflow, so do not publish a single expected score without a benchmark for your dataset, model version, and split.
Current model facts and deployment caveats
NVIDIA’s API model table lists KumoTabular at 27.46 million to 215.68 million parameters, with a listed release date of September 28, 2026. Those figures describe the named model range in that table, not every SDM release or a guaranteed memory requirement. Confirm the exact image, hardware requirements, context limits, and license before committing to an installation.
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The practical trade-off is straightforward: local execution gives you control over data movement and runtime, while NIM centralizes serving and client access. Neither option removes the need to define a valid predictive question and supply representative labeled context.
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