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However, GPUs come with tradeoffs—they are more expensive, less readily available than CPUs, and require specialized programming knowledge. While they significantly outperform CPUs in parallel workloads, they are less efficient for tasks involving complex logic or sequential decision-making. In practice, GPUs act as accelerators, working alongside CPUs to handle compute-heavy operations while the CPU manages orchestration and control.