Traditional data‑science workflows running on CPUs are slow and resource‑intensive for tasks such as loading, filtering, and modeling large datasets. GPUs, combined with RAPIDS open‑source libraries, dramatically accelerate end‑to‑end data‑science pipelines while lowering infrastructure costs. GPU‑powered workflows can run on laptops, data centers, or cloud environments, and a single GPU node can replace the work of up to 100 CPU nodes. By shifting from CPU clusters to GPU‑based systems, organizations can speed up data‑science workloads by more than 100 times while reducing operational complexity and improving efficiency.

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