IBM and NVIDIA Expand Partnership to Push Enterprise AI Beyond Experimentation

At GTC 2026, IBM and NVIDIA announced an expanded collaboration aimed at helping enterprises move artificial intelligence projects from experimentation into production.

The partnership spans data infrastructure, analytics, AI models, and consulting services, with the stated goal of enabling organizations to operationalize AI at scale.

Despite growing investments, many companies remain stuck in pilot phases. According to the companies, common barriers include fragmented data, infrastructure not designed for advanced AI workloads, regulatory and data residency constraints, and a lack of implementation expertise.

GPU-native analytics: turning data into faster decision-making

A central focus of the collaboration is accelerating structured data analytics using GPU-native computing.

IBM's watsonx.data platform is being integrated with NVIDIA technologies, including cuDF acceleration for the Presto SQL engine, to improve performance when querying large datasets.

In a proof-of-concept deployment with Nestlé, the companies tested GPU-accelerated analytics on an order-to-cash data mart covering global operations.

According to IBM and NVIDIA:

• Query runtime was reduced from 15 minutes to 3 minutes
• Costs decreased by 83%
• Price-performance improved by up to 30x

While the results suggest significant efficiency gains, they are based on a targeted use case rather than broad enterprise-wide deployment.

Addressing unstructured data: from documents to AI-ready inputs

The partnership also focuses on extracting value from unstructured data, a persistent challenge for enterprise AI.

Organizations often store information across documents, internal systems, and content platforms, making it difficult to standardize and use in AI workflows.

To address this, IBM and NVIDIA are combining:

• IBM Docling, designed to standardize and convert documents into AI-ready formats with traceability
• NVIDIA Nemotron models, used to process multi-modal content

IBM and NVIDIA say early tests show higher throughput compared to some open-source models in internal testing, although detailed benchmarks and testing conditions were not disclosed.

Infrastructure and compliance: enabling AI in regulated environments

The companies are also expanding collaboration at the infrastructure level, particularly for enterprises operating under regulatory constraints.

This includes:

• GPU-optimized storage systems combining IBM Storage Scale with NVIDIA DGX platforms
• Exploration of integrations between IBM Sovereign Core and NVIDIA infrastructure
• Support for AI workloads that can run within regional boundaries to meet data residency requirements

These efforts are intended to support industries such as government, finance, and healthcare, where compliance and data control are critical.

Cloud and consulting: building a full enterprise AI stack

IBM also plans to offer NVIDIA Blackwell Ultra GPUs on IBM Cloud starting in early Q2 2026, targeting large-scale training, high-throughput inference, and AI reasoning and inference workloads.

The companies say these capabilities will be integrated with Red Hat AI Factory with NVIDIA and IBM's consulting services.

IBM says its consulting platform is designed to help organizations prepare data, build models, and deploy AI systems across environments, while maintaining governance and oversight.

Financial terms of the expanded partnership were not disclosed.

What this means for enterprise AI

The announcement reflects what many industry players describe as a broader shift in AI adoption, from building models toward integrating AI into core business operations.

The focus is increasingly on:

• Data accessibility and readiness
• Infrastructure capable of supporting AI workloads
• Deployment at scale
• Governance and compliance

In that context, the IBM–NVIDIA collaboration centers on connecting these layers into a usable enterprise system.

The bigger picture: AI as core infrastructure

NVIDIA CEO Jensen Huang emphasized the importance of data in this transition:

As AI adoption matures, the challenge is shifting from experimentation to integration, and from isolated use cases to organization-wide systems.