At NVIDIA GTC, IBM, Nvidia and Nestlé showcased what’s possible when GPU acceleration meets open data architecture—demonstrating up to 5x faster analytics workloads while reducing costs by as much as 83%. It was a clear signal of what the future of data-intensive workloads can look like when performance and efficiency are rethought together.

Today, IBM is building on that momentum with the official announcement of the private technical preview of GPU-accelerated query processing for watsonx.data—designed to help enterprises tackle one of their most pressing challenges: the rising cost of analytics and AI at scale.

As organizations continue to expand AI and analytics across increasingly complex, distributed data environments, traditional CPU-based systems are struggling to keep pace. The result is slower insights, escalating infrastructure costs and growing pressure on data teams to deliver more value with fewer resources. GPU acceleration represents a new path forward.

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