Fundamental’s Large Tabular Model NEXUS is now available on Amazon SageMaker JumpStart

Today, we’re announcing support for Fundamental’s NEXUS model on Amazon SageMaker AI. With this launch, you can deploy a foundation model (FM) purpose-built for tabular data prediction. This model helps your enterprise generate accurate, deterministic predictions from structured data in days instead of months.

In this post, we show you how to get started with NEXUS on Amazon SageMaker JumpStart, walk through the deployment process, and demonstrate how to run predictions against your enterprise datasets.

NEXUS is a foundation model developed by Fundamental and built for tabular data prediction. Large language models (LLMs) are designed for text, and traditional machine learning (ML) approaches require extensive feature engineering and model training. NEXUS takes a different approach. It’s pre-trained on billions of real-world prediction tasks across structured datasets, so it arrives already knowing how to find signal in your data.

Deterministic architecture – Probabilistic LLMs might provide different answers to identical queries. NEXUS produces consistent, reproducible results for each individual prediction.

Native tabular understanding – Trained on billions of tables, NEXUS natively processes numbers, categories, dates, and unstructured text without manual feature engineering.

Non-sequential reasoning – Most AI models predict sequential data (for example, the next word or the next pixel). NEXUS analyzes multi-dimensional relationships in enterprise tables. For example, when predicting customer churn, NEXUS understands how multiple factors (transaction frequency, support tickets, and economic indicators) impact the likelihood of attrition.

The most valuable enterprise data sits in tables such as spreadsheets, enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and relational databases. Many critical business decisions depend on predictions made against this data. However, today’s tools have significant limitations:

Traditional ML takes teams of data scientists 3–6 months to build, train, and deploy a model for a single use case. You face a constant trade-off between quality and quantity of predictions.

LLMs are non-deterministic, producing different answers on the same dataset. They lose numerical context during tokenization, which leads to inaccurate results on structured data and requires complex guardrails to mitigate these issues.

Permutation invariance – Recognizes that changing column order doesn’t change meaning, which differs from how transformers handle data.

Billion-row capability – Processes massive datasets without truncation or sampling.

Cross-schema reasoning – Connects related data across disparate tables automatically.

Autonomous data cleaning – Resolves incomplete entries (for example, NEXUS can still make predictions even when entries are missing).

The following figure illustrates the end-to-end flow for deploying and running predictions with NEXUS on SageMaker AI.

NEXUS runs on a dedicated, single-tenant, network-isolated GPU instance within the SageMaker AI managed environment. The workflow consists of the following steps:

Subscribe and deploy – Subscribe to the NEXUS model package on AWS Marketplace, then deploy it as a SageMaker AI managed inference endpoint on an ml.p5en.48xlarge instance (8× NVIDIA H200 GPUs).

Install the SDK – Install the Fundamental Python SDK and connect it to your SageMaker endpoint. The SDK provides a familiar scikit-learn compatible API with NEXUSClassifier and NEXUSRegressor estimators.

Upload data to Amazon S3 – The SDK serializes your tabular data and uploads it to an Amazon Simple Storage Service (Amazon S3) bucket in your account.

Train a model – Call clf.fit(X_train, y_train) to train. NEXUS handles data cleanup and feature engineering automatically, with no manual pipeline required.

Generate predictions – Call clf.predict(X_test) for deterministic predictions or clf.predict_proba(X_test) for probability estimates. Results are stored back in your Amazon S3 bucket.

Your data stays in your AWS environment throughout this process. The endpoint is network-isolated and single-tenant, which makes NEXUS suitable for enterprise workloads with sensitive data.

Base model (pre-trained on over 10B tabular rows).

Industry-specific variants (finance, healthcare, and manufacturing).

Tabular data is the backbone of enterprise decision-making, from financial ledgers to patient records to supply chain logs. NEXUS is purpose-built for this data and helps you go from raw structured data to production-grade predictions without extensive feature engineering or model training. The following are a few representative use cases where NEXUS can create value.

Fraud detection – Analyzes transaction patterns across millions of accounts.

Credit risk modeling – Processes loan portfolios with automated feature extraction.

Regulatory compliance – Extracts structured data from unstructured regulatory filings.

Clinical trial matching – Identifies eligible patients across electronic health record (EHR) systems.

Drug discovery – Analyzes biological assay data for compound screening.

Patient risk stratification – Predicts readmission risks using intensive care unit (ICU) time-series data.

Predictive maintenance – Forecasts equipment failures from sensor data.

Demand forecasting – Anticipates inventory needs across global distribution networks.

Supplier risk analysis – Evaluates vendor reliability using procurement history.

Churn prediction – Identifies at-risk customers by using purchase history and browsing behavior.

Dynamic pricing – Optimizes prices based on competitor data and inventory levels.

Cart abandonment analysis – Helps you understand why customers leave items in online carts.

Deploying a model is only half the equation. The infrastructure you run it on determines how quickly you can move from experimentation to production. SageMaker AI provides a managed, secure, and scalable environment for running NEXUS at enterprise scale. Together, NEXUS and AWS reduce undifferentiated heavy lifting so your data scientists can focus on business outcomes rather than infrastructure management.

Accelerated time-to-value – Pre-built containers and scripts reduce deployment time.

Cost efficiency – The managed infrastructure of SageMaker AI reduces operational overhead.

Scalability – Automatically scales to petabyte-scale datasets.

Compliance ready – Meets GDPR, HIPAA, and SOC 2 requirements by default.

Continuous learning – Native integration with Amazon SageMaker Pipelines for model retraining.

Multiplex support – Supports multiple fit and predict operations on a single SageMaker AI endpoint, which removes the need for dedicated resources for each use case.

Native integration – Deploy NEXUS directly from AWS Marketplace.

Secure infrastructure – Runs on the AWS secure, compliant cloud environment.

Enterprise support – Dedicated AWS Solutions Architects for implementation guidance.

Ready to transform your data-driven decisions?

Contact the Fundamental team to learn more.

Try the managed example notebook in a JupyterLab space on Amazon SageMaker AI.

In this post, we showed how NEXUS model support on Amazon SageMaker AI helps you unlock new insights from your structured data assets. Whether you’re predicting equipment failures, optimizing supply chains, or detecting financial fraud, NEXUS provides deterministic, scalable capabilities for your enterprise prediction workloads.