Definition – CPU handles general-purpose tasks, while GPU specializes in parallel computing and rendering graphics for complex workloads and visual applications.
Core Count – CPUs usually have fewer, powerful cores, while GPUs have thousands of smaller cores for parallel data processing efficiency.
Performance – CPUs excel at sequential tasks and logic-heavy processes, while GPUs outperform in parallel tasks like rendering and AI.
Architecture – CPU architecture focuses on versatility and control, whereas GPU architecture prioritizes throughput and simultaneous data processing for faster results.
Applications – CPUs power operating systems, apps, and multitasking, while GPUs dominate gaming, AI, deep learning, and video rendering industries.
Speed – CPUs manage fast switching between tasks, but GPUs achieve higher processing throughput in specialized, repetitive workloads requiring parallelism.
Flexibility – CPUs handle diverse, unpredictable tasks well; GPUs are optimized for specific repetitive tasks, offering less flexibility but better performance.
Energy Use – CPUs consume moderate energy, while GPUs can be power-intensive due to their massive parallel processing capabilities and higher workload.
Cost – CPUs are essential and relatively affordable; GPUs are often expensive due to specialized design, advanced technology, and high demand.