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IBM and NVIDIA have expanded their collaboration to help enterprises deploy artificial intelligence at scale, as many organizations struggle to move beyond pilot projects.
The announcement, made at Nvidia GTC 2026, focuses on integrating data, infrastructure and AI tools to address common barriers such as fragmented data, legacy systems and regulatory constraints.
Arvind Krishna said the next phase of enterprise AI will depend on how effectively companies connect data, infrastructure and orchestration layers to support real-world deployments.
Jensen Huang said the partnership aims to embed GPU acceleration deeper into enterprise data systems, enabling faster analytics and real-time decision-making.
A key part of the collaboration is the use of GPU acceleration in data analytics. IBM’s watsonx.data platform is being integrated with NVIDIA technologies to improve performance in processing large datasets.
In a production test with Nestlé, the companies said query processing times were reduced from 15 minutes to about three minutes, delivering 83% cost savings and a 30-fold improvement in price-performance.
The partnership also targets unstructured data, which remains difficult for many organizations to use effectively. IBM’s document processing tools are being combined with NVIDIA’s AI models to convert complex documents into structured, AI-ready formats.
On the infrastructure side, the companies are working to support AI deployments in regulated environments. This includes integrating IBM’s sovereign cloud capabilities with NVIDIA’s AI infrastructure to enable data residency and compliance requirements for enterprises and governments.
IBM will also offer NVIDIA’s next-generation GPUs, including Blackwell-based systems, on its cloud platform starting in 2026 to support large-scale AI training and inference workloads.
The collaboration extends to enterprise consulting and open-source ecosystems through technologies such as Red Hat, with a focus on simplifying how companies build, deploy and manage AI systems.
The expanded partnership reflects a broader industry push to operationalize AI, as enterprises shift from experimentation toward production deployments that deliver measurable business outcomes.
The announcement, made at Nvidia GTC 2026, focuses on integrating data, infrastructure and AI tools to address common barriers such as fragmented data, legacy systems and regulatory constraints.
Arvind Krishna said the next phase of enterprise AI will depend on how effectively companies connect data, infrastructure and orchestration layers to support real-world deployments.
Jensen Huang said the partnership aims to embed GPU acceleration deeper into enterprise data systems, enabling faster analytics and real-time decision-making.
A key part of the collaboration is the use of GPU acceleration in data analytics. IBM’s watsonx.data platform is being integrated with NVIDIA technologies to improve performance in processing large datasets.
In a production test with Nestlé, the companies said query processing times were reduced from 15 minutes to about three minutes, delivering 83% cost savings and a 30-fold improvement in price-performance.
The partnership also targets unstructured data, which remains difficult for many organizations to use effectively. IBM’s document processing tools are being combined with NVIDIA’s AI models to convert complex documents into structured, AI-ready formats.
On the infrastructure side, the companies are working to support AI deployments in regulated environments. This includes integrating IBM’s sovereign cloud capabilities with NVIDIA’s AI infrastructure to enable data residency and compliance requirements for enterprises and governments.
IBM will also offer NVIDIA’s next-generation GPUs, including Blackwell-based systems, on its cloud platform starting in 2026 to support large-scale AI training and inference workloads.
The collaboration extends to enterprise consulting and open-source ecosystems through technologies such as Red Hat, with a focus on simplifying how companies build, deploy and manage AI systems.
The expanded partnership reflects a broader industry push to operationalize AI, as enterprises shift from experimentation toward production deployments that deliver measurable business outcomes.
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