CPU vs GPU vs TPU: Key Differences for AI and Computing

Choosing the right processor is essential for different computing tasks, especially with the rise of AI and machine learning. Let’s compare CPU, GPU, and TPU to understand their strengths.

CPU (Central Processing Unit)

• Purpose: General-purpose processor for most computing tasks.
• Architecture: Few cores (4–32).
• Strength: Best for serial processing and low-latency tasks.
• Use Cases: Operating systems, web browsing, and backend servers.

GPU (Graphics Processing Unit)

• Purpose: Originally for graphics, now used in parallel processing.
• Architecture: Hundreds to thousands of cores.
• Strength: Ideal for tasks requiring massive parallelism.
• Use Cases: Deep learning, video rendering, and scientific simulations.

TPU (Tensor Processing Unit)

• Purpose: Specialized for machine learning, particularly TensorFlow.
• Architecture: Optimized for tensor operations.
• Strength: Extremely efficient for deep learning at scale.
• Use Cases: AI training and inference, especially in large-scale AI projects.

Which to Choose?

• CPU: General computing tasks.
• GPU: AI, video, and graphics-heavy tasks.
• TPU: Large-scale TensorFlow ML tasks.

In conclusion, the choice depends on your specific project needs, balancing general tasks (CPU), parallel workloads (GPU), or deep learning (TPU).