IBM and NVIDIA Unite to Propel Enterprise AI Forward

International Business Machines Corp (IBM) has announced a significant expansion of its collaboration with NVIDIA, aimed at helping enterprises operationalize artificial intelligence (AI) at scale. This news, unveiled during NVIDIA's GTC 2026 event, underscores a shared commitment to bridge the gap between AI experimentation and production across various industries. By addressing challenges related to fragmented data, inadequate infrastructure, and compliance requirements, the partnership seeks to empower businesses to harness AI effectively.

Despite substantial investments in AI, many organizations find themselves stalled between the stages of experimentation and full-scale production. Key barriers identified include:

IBM and NVIDIA aim to tackle these challenges head-on, creating a more seamless pathway for enterprises to transition from pilot projects to operational AI.

Arvind Krishna, Chairman and CEO of IBM, highlighted the importance of integrating data, infrastructure, and orchestration layers for successful AI deployment. "Together, we're giving enterprises the solutions they need to stop experimenting with AI and start running on it," he stated.

Jensen Huang, founder and CEO of NVIDIA, emphasized the transformative power of data, asserting that it provides the essential context for AI. "Together with IBM, we are bringing CUDA GPU acceleration directly into the data layer," he added, aiming to turn data analytics and document processing into real-time intelligence engines.

A focal point of the collaboration is the enhancement of structured data analytics through GPU-native computing. IBM and NVIDIA are working on an open-source integration that significantly increases performance and reduces costs for enterprises extracting intelligence from vast datasets. IBM's watsonx.data SQL engine, accelerated by NVIDIA cuDF, offers faster query execution for large datasets.

In a practical demonstration, the duo applied GPU-accelerated watsonx.data to Nestlé's Order-to-Cash data mart, which manages orders across 186 countries. This proof of concept showcased remarkable results: query runtime was reduced from 15 minutes to just three minutes, yielding an 83% cost savings and a 30-fold improvement in price-performance.

Chris Wright, Chief Information and Digital Officer of Nestlé, remarked on the importance of data in decision-making, noting that the rapid refresh of global operations data can significantly enhance decision speed in critical areas such as manufacturing and warehousing.

IBM and NVIDIA are also addressing the challenge of extracting value from unstructured data. Their collaboration introduces Docling from IBM and NVIDIA Nemotron open models, which aim to make intelligent document extraction scalable across enterprises. By standardizing documents into AI-ready formats and accelerating the ingestion of multi-modal content, early results show substantial improvements in throughput and accuracy.

As part of their strategic partnership, IBM and NVIDIA are enhancing infrastructure capabilities. NVIDIA has selected the IBM Storage Scale System 6000, which provides a robust 10PB of high-performance storage for GPU-native analytics. This integration combines IBM's unified data access with NVIDIA's GPU pipelines, optimizing data processing for diverse enterprise needs.

For organizations requiring regulatory compliance, both companies are exploring the integration of IBM Sovereign Core and NVIDIA infrastructure to facilitate GPU-intensive AI workloads that respect regional governance.

The collaboration extends to cloud and consulting services, with IBM set to offer NVIDIA Blackwell Ultra GPUs on IBM Cloud by early Q2 2026. This will support large-scale training, high-throughput inferencing, and AI reasoning. Additionally, IBM Consulting plans to leverage Red Hat AI Factory to assist clients in building and scaling AI across their technology environments, simplifying model deployment while enhancing performance and governance.

IBM and NVIDIA's expanded partnership represents a strategic move to revolutionize the enterprise AI landscape, offering organizations the tools and expertise needed to transition from AI experimentation to impactful production. By addressing core challenges in data accessibility, infrastructure capabilities, and compliance requirements, this collaboration is poised to empower enterprises to unlock the full potential of their data and drive meaningful business outcomes.