IBM and NVIDIA announced an expanded partnership designed to solve a critical problem: most enterprises are stuck experimenting with AI instead of running it at scale. The collaboration addresses the real barriers holding companies back, including fragmented data, infrastructure not built for AI workloads, and compliance challenges in regulated industries. The partnership spans GPU-accelerated data analytics, intelligent document processing, on-premises infrastructure, cloud deployment, and consulting services .

Why Are Companies Struggling to Move AI From Pilot to Production?

Enterprises are investing heavily in artificial intelligence, but many remain trapped between experimentation and actual deployment. The obstacles are consistent across industries. Data sits scattered across multiple systems and formats, making it difficult to access quickly. Existing infrastructure wasn't designed to handle the computational demands of advanced AI workloads. Regulated industries face additional hurdles around data residency and compliance requirements. And many organizations lack the internal expertise to implement and deploy these technologies at scale .

"In the next wave of enterprise AI, the model layer will rely on the data, infrastructure, and orchestration layers, and on businesses that can bring all three together," said Arvind Krishna, Chairman and CEO of IBM.

Arvind Krishna, Chairman and CEO, IBM

Jensen Huang, founder and CEO of NVIDIA, emphasized the importance of data infrastructure in this partnership. "Data is the ground truth that gives AI context and meaning," he stated. "Together with IBM, we are bringing CUDA GPU acceleration directly into the data layer, turning analytics and document processing from bottlenecks into real-time intelligence engines" .

How Is GPU Acceleration Transforming Data Analytics?

IBM and NVIDIA are collaborating on an open-source integration that dramatically speeds up how enterprises extract insights from massive datasets. IBM's watsonx.data platform, which uses a SQL engine called Presto, is now accelerated by NVIDIA's cuDF technology to enable faster query execution on large datasets. The real-world impact is striking. At Nestlé, a global food company that operates in 186 countries, the partnership delivered measurable results .

Nestlé's Order-to-Cash data mart tracks every order, fulfillment, delivery, and invoice across the company's global operations, processing terabytes of data across 44 tables. Previously, on traditional CPU-based systems, a single data refresh took 15 minutes and could only run a handful of times per day. With NVIDIA's GPU acceleration and IBM's watsonx.data platform, that same refresh now completes in just three minutes. The company achieved an 83% reduction in costs and a 30-fold improvement in price-to-performance ratio .

"For a company that serves billions, data underpins decision making across our global operations," explained Chris Wright, Chief Information and Digital Officer of Nestlé. "Working with IBM and NVIDIA, a targeted proof of concept has demonstrated the ability to refresh global operations data in a few minutes and at reduced cost."

Chris Wright, Chief Information and Digital Officer, Nestlé

What Are the Key Components of the IBM-NVIDIA Partnership?

• GPU-Native Data Analytics: IBM's watsonx.data platform accelerated by NVIDIA's cuDF technology enables faster query execution on large datasets, reducing processing time and costs for enterprises managing massive amounts of structured data.
• Intelligent Document Processing: IBM's Docling tool combined with NVIDIA's Nemotron open models makes it possible to extract and standardize unstructured data from documents, emails, and other sources at enterprise scale with higher throughput and maintained accuracy.
• Infrastructure for Regulated Industries: IBM Storage Scale System 6000 provides high-performance storage paired with NVIDIA GPU pipelines, while IBM Sovereign Core integration enables GPU-intensive AI workloads to run entirely within regional boundaries for compliance and data residency requirements.
• Cloud and Consulting Services: IBM plans to offer NVIDIA Blackwell Ultra GPUs on IBM Cloud starting in early Q2 2026, integrated with Red Hat AI Factory and supported by IBM Consulting's enterprise AI platform.

How Can Enterprises Unlock Value From Unstructured Data?

Most enterprises aren't lacking data. The real problem is that much of their valuable information is trapped in unstructured formats. SharePoint sites, content management systems, vendor research documents, and subject matter expert knowledge exist within organizations but remain difficult to extract, standardize, and use at decision speed. IBM and NVIDIA are addressing this challenge through a combination of tools designed to make intelligent document extraction available at enterprise scale .

IBM's Docling standardizes and converts documents into AI-ready formats while maintaining source-level traceability, showing exactly where information came from. NVIDIA's Nemotron open models accelerate the ingestion of multi-modal content, including text, images, and other formats. Early results show significantly higher throughput compared to other open-source models while maintaining or improving accuracy wherever GPU-accelerated infrastructure is available .

What Infrastructure Changes Are Needed for Enterprise AI at Scale?

IBM and NVIDIA are extending their collaboration to the infrastructure layer, recognizing that data analytics and document processing are only part of the solution. NVIDIA has selected IBM Storage Scale System 6000 to provide 10 petabytes of high-performance storage to serve massive data for GPU-native advanced analytics engines. This pairs IBM's unified data access layer and massive parallel throughput with NVIDIA's GPU pipelines. The IBM Storage Scale 6000 is certified and validated on NVIDIA DGX platforms .

For enterprises and governments requiring strict data residency and regulatory control, IBM and NVIDIA are exploring integration of IBM Sovereign Core with NVIDIA infrastructure and Nemotron models. This approach would enable GPU-intensive AI workloads to run entirely within regional boundaries without compromising governance or compliance requirements, addressing a critical need for regulated industries like finance, healthcare, and government .

When Will These Solutions Be Available on Cloud Platforms?

IBM plans to offer NVIDIA Blackwell Ultra GPUs on IBM Cloud in early Q2 2026 for large-scale training, high-throughput inferencing, and AI reasoning tasks. This technology will also be integrated across Red Hat AI Factory with NVIDIA and VPC servers with enterprise-grade compliance and data residency controls. IBM Consulting plans to bring Red Hat AI Factory with NVIDIA to clients through IBM Consulting Advantage, an enterprise AI platform designed to simplify how companies prepare data, build models, and deploy AI while enhancing performance and oversight .

The partnership builds on IBM Consulting's broader efforts to help clients maximize outputs from their AI investments. By combining infrastructure, data tools, and consulting expertise, IBM and NVIDIA are attempting to address the complete journey from data preparation through model deployment and ongoing optimization. This comprehensive approach reflects the reality that successful enterprise AI requires more than just powerful hardware or software, it requires alignment across data strategy, infrastructure, and organizational capability.