CPU vs GPU vs TPU vs NPU
Comparing hardware architectures for machine learning workloads.
Verdict: Use CPUs for sequential logic and data prep; use GPUs for training and flexible model deployment; use TPUs for massive-scale training; use NPUs for low-power edge inference on mobile.
The Short Answer
A CPU (Central Processing Unit) is a generalist with 8-64 very smart cores designed to handle highly branching logic (if/else statements). A GPU (Graphics Processing Unit) is a specialist with thousands of dumb cores designed to do the same simple math operation (like multiplying matrices) on massive blocks of data simultaneously. TPUs (Tensor Processing Units) and NPUs (Neural Processing Units) are ASICs—custom silicon that strips away almost all general logic to perform pure tensor math at maximum efficiency.
Where They Differ
| Feature | CPU | GPU | TPU | NPU |
|---|---|---|---|---|
| Architecture | Few, complex cores | Thousands of simple cores | Matrix multiply grids | Low-power matrix grids |
| Best at | Sequential logic, branching | Massively parallel matrix math | Google Cloud scale training | On-device inference |
| Memory Bandwidth | Low (DDR5) | Extremely High (HBM3) | Extremely High | Shared with device |
| Vendor | Intel, AMD, ARM | NVIDIA, AMD | Apple, Qualcomm |
Choose a CPU When
- You are doing data preprocessing: Loading CSVs, filtering rows, and running Python scripts requires heavy sequential logic that GPUs are terrible at.
- Your model is small: If you are running a classic Random Forest or a tiny 1B parameter model with few concurrent users, CPU inference is often cheaper and perfectly adequate.
Choose a GPU When
- You are training deep learning models: Neural networks are entirely built on matrix multiplication. A GPU can perform these multiplications thousands of times faster than a CPU. NVIDIA GPUs (via CUDA) are the undisputed industry standard, meaning 100% of open-source code will work flawlessly on them.
Choose a TPU When
- You are training at massive scale on Google Cloud: TPUs are locked inside Google Cloud. They use a specialized architecture (Systolic Arrays) that passes data directly between computation units without writing to memory. If you are training a massive LLM from scratch, TPUs often offer better price-to-performance than NVIDIA GPUs, provided your code is written in JAX or PyTorch/XLA.
Choose an NPU When
- You are deploying to consumer devices: NPUs are built into modern smartphone chips (Apple Neural Engine) and laptops. They allow the device to run models (like FaceID or local LLMs) without draining the battery or spinning up cooling fans.
What People Get Wrong
People often assume GPUs are fundamentally "faster" than CPUs. They aren't. A single CPU core is much faster (higher clock speed) than a single GPU core. GPUs only win because neural networks require doing the exact same mathematical operation millions of times in parallel. If you try to run complex, branching for-loops on a GPU, it will actually run much slower than a CPU.