At GTC 2026, IBM and NVIDIA announced a significant expansion of their more than decade-long partnership, focusing on moving AI from pilot phases to full-scale production. The collaboration targets several critical bottlenecks in enterprise AI adoption, including GPU-native data analytics, intelligent document processing, and infrastructure for regulated environments. The joint effort aims to provide a unified stack of data foundations, hardware, and consulting expertise to help organizations manage fragmented data and compliance requirements.
IBM Chairman and CEO Arvind Krishna noted that the next phase of enterprise AI depends on the tight integration of data, infrastructure, and orchestration. He stated that the partnership is designed to provide the necessary components for businesses to transition from experimentation to operational deployment. NVIDIA founder and CEO Jensen Huang highlighted that by integrating CUDA acceleration into the data layer, the companies are attempting to turn traditional data processing into real-time intelligence engines.
Accelerating Structured Data Analytics via cuDF and Presto
A primary technical focus of the announcement is the integration of the NVIDIA cuDF library with the Presto SQL engine in IBM watsonx.data. This open-source integration enables GPU-accelerated query execution on massive datasets, significantly reducing the time and cost of extracting intelligence from structured data.
The companies validated this approach in a production environment with Nestlé’s Order-to-Cash data mart, which processes terabytes of data across 44 tables globally. On traditional CPU infrastructure, a single data refresh took 15 minutes and was limited to a few cycles per day. By moving to the GPU-accelerated watsonx.data engine, Nestlé reported a reduction in query runtime to three minutes. This represents a 30X improvement in price-performance and an 83 percent reduction in costs. Nestlé’s Chief Information and Digital Officer, Chris Wright, indicated that this capability enables faster decision-making in manufacturing and warehousing by providing near-real-time operational data.
Unlocking Unstructured Data with Docling and Nemotron
To address the challenge of data trapped in unstructured formats such as SharePoint sites and CMS systems, IBM and NVIDIA introduced a joint solution leveraging IBM Docling and NVIDIA Nemotron open models. Docling is designed to standardize and convert complex documents into AI-ready formats while maintaining traceability to the source.
When paired with NVIDIA Nemotron models, the system accelerates the ingestion of multi-modal content. Early testing indicates significantly higher throughput compared to existing open-source models. This approach is intended to help enterprises build a trusted data foundation for AI by making internal knowledge bases more accessible and easier to standardize for automated reasoning.
GPU-Optimized Infrastructure and Sovereign AI
The partnership also extends deep into the storage and infrastructure layers. NVIDIA has selected the IBM Storage Scale System 6000 to provide 10 PB of high-performance storage for its GPU-native advanced analytics engines. The Storage Scale 6000 is certified and validated for NVIDIA DGX platforms, combining IBM’s parallel throughput capabilities with NVIDIA’s GPU data pipelines to eliminate I/O bottlenecks.
Organizations with strict data residency and regulatory requirements are exploring the integration of IBM Sovereign Core with NVIDIA infrastructure. This initiative aims to enable GPU-intensive AI workloads to run entirely within specific regional borders, ensuring governance and compliance standards are met without sacrificing compute performance.
Expanding the AI Stack with Blackwell and Red Hat
IBM announced plans to introduce NVIDIA Blackwell Ultra GPUs to the IBM Cloud in early Q2 2026. These resources will be targeted at large-scale model training and high-throughput inference. The Blackwell architecture will also be integrated into the Red Hat AI Factory, alongside NVIDIA and VPC servers, to provide enterprise-grade controls over data residency.
Furthermore, IBM Consulting will offer the Red Hat AI Factory with NVIDIA through the IBM Consulting Advantage platform. This move is intended to streamline the process of data preparation, model creation, and deployment. By combining these technologies, IBM and NVIDIA are attempting to provide a more seamless path for enterprises to scale AI across diverse technology environments while maintaining oversight and performance.
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