Fundamentals

GPU vs CPU: What's the Difference?

CPUs (Central Processing Units) excel at sequential, complex tasks with a few powerful cores. GPUs (Graphics Processing Units) excel at parallel tasks with thousands of smaller cores. For AI workloads, GPUs are typically 10-100x faster than CPUs because neural network training and inference rely heavily on parallel matrix multiplication — exactly what GPUs are designed for.

Architecture Differences

A modern CPU like an Intel Xeon or AMD EPYC has 32-128 cores, each capable of complex independent operations. A modern GPU like the NVIDIA H100 has thousands of CUDA cores plus hundreds of specialised Tensor Cores, all designed to execute the same operation on different data simultaneously.

This difference in architecture means CPUs are better for tasks with complex branching logic (web servers, databases, general applications), while GPUs are better for tasks that apply the same operation to large datasets (AI training, graphics rendering, scientific simulation).

Why GPUs Dominate AI

Neural network training is fundamentally a series of large matrix multiplications — exactly the type of parallel workload GPUs excel at. A single H100 GPU delivers 990 FP16 TFLOPS, while even the fastest CPU manages single-digit TFLOPS for equivalent operations.

This performance gap means an AI training job that takes hours on GPUs might take weeks or months on CPUs. The economics are clear: even though GPUs cost more per hour, they complete work so much faster that the total cost is dramatically lower.

When to Use CPU vs GPU

Use CPUs for: data preprocessing, web serving, databases, ETL pipelines, and workloads with complex logic and small batch sizes.

Use GPUs for: model training, model inference (especially batched), image/video processing, and any workload involving large-scale matrix operations.

Many AI workflows use both — CPUs for data preparation and orchestration, with GPUs handling the compute-intensive training and inference steps.

Frequently Asked Questions

Is a GPU faster than a CPU for AI?

Yes, dramatically. For AI workloads, GPUs are typically 10-100x faster than CPUs due to their parallel architecture. The NVIDIA H100 delivers 990 FP16 TFLOPS compared to single-digit TFLOPS on the fastest CPUs.

Can I train AI models on a CPU?

Technically yes, but it's extremely slow. A training job that takes hours on a GPU could take weeks on a CPU. CPUs are only practical for very small models or experimentation.

Do I need a GPU for AI inference?

For production inference at scale, yes. While CPUs can run inference for some models, GPUs provide much higher throughput and lower latency. Budget GPUs like the T4 (from ~$0.25/hr) make GPU inference cost-effective for most workloads.