The basic differences between CPUs and GPUs

When designing Datacentres, our team regularly gets asked what the difference is between CPUs (Central Processing Units) and |GPUs (Graphics Processing Units) requirements, here is a basic overview.

CPUs are designed for general-purpose, sequential processing with a few powerful cores, while GPUs are built for massive parallel processing with thousands of smaller cores optimized for tasks like graphics rendering, AI, and scientific simulations.

Feature	CPU	GPU
Architecture	Few powerful cores (2–64+)	Thousands of smaller, simpler cores
Processing Style	Sequential, optimized for single-thread performance	Parallel, optimized for handling many tasks simultaneously
Primary Role	General-purpose computing: OS, applications, logic, arithmetic	Specialized computing: graphics rendering, deep learning, simulations
Strengths	- Versatility (can run any program)- Strong single-thread performance- Excellent multitasking	- High throughput- Massive parallelism- Superior for repetitive computations (e.g., matrix math, image rendering)
Weaknesses	- Limited parallelism- Higher power consumption for intensive tasks	- Less versatile- Not ideal for sequential or logic-heavy tasks
Memory	Uses system RAM with cache hierarchy (L1, L2, L3)	Uses dedicated VRAM (Video RAM) for high-speed data access
Best Use Cases	Running operating systems, databases, office apps, simulations requiring precision	Gaming, video editing, AI/ML training, scientific modeling, cryptocurrency mining

• CPU ("the brain of the computer")

  • Handles general-purpose tasks like running operating systems, browsers, and productivity software.

  • Optimized for sequential execution: fetch → decode → execute → store.

  • Strong at tasks requiring accuracy, order, and complex logic, such as database queries or code compilation.

  • Features cache hierarchies and high clock speeds to minimize latency.

• GPU ("the muscle of parallel computing")

  • Originally designed for graphics rendering, now widely used in AI, deep learning, and scientific simulations.

  • Excels at parallel workloads: thousands of cores crunching repetitive computations simultaneously.

  • Uses VRAM, separate from system RAM, enabling faster throughput for large datasets.

  • Ideal for matrix operations, image/video processing, and neural networks.

• If you’re running general applications (Word, Excel, OS tasks), the CPU is indispensable.

• If you’re working with AI models, 3D rendering, or scientific simulations, the GPU provides unmatched speed.

• Modern systems often combine both: CPU for orchestration and logic, GPU for heavy lifting.

Two Sample Areas where CPUs and GPUs work together:

• Self-driving cars:

  • CPU manages sensors, navigation, and decision-making.

  • GPU processes camera feeds and LIDAR data in real time for object detection.

• AI research:

  • CPU coordinates distributed training across multiple GPUs.

  • GPUs perform the actual model training computations.