The final guide to CPU, GPU, NPU and TPU for AI/ML: Performance, Use Cases, and Key Differences

Artificial intelligence and machine learning workloads drive the evolution of professional hardware to accelerate computing, far beyond what traditional CPUs can offer. Each processing unit (CPU, GPU, NPU, TPU) plays a unique role in the AI ecosystem, optimizing certain models, applications, or environments. This is its core difference and best use cases, a data-driven segmentation.

CPU (Central Processing Unit): Multi-function main force

• Design and Advantages: The CPU is a general purpose processor with some powerful cores for single-line reading tasks and running a variety of software, including operating systems, databases, and optical AI/ML inference.
• AI/ML roles: The CPU can execute any type of AI model, but lacks the massive parallelism required for effective deep learning training or large-scale inference.
• Best for:
  • Classic ML algorithms (e.g. Scikit-Learn, XGBoost)
  • Prototype development and model development
  • Inferences of small models or low throughput requirements

Technical Note: For neural network operations, CPU throughput (usually measured in GFLOPS (5 billion floating point operations per second) lags far behind professional accelerators.

GPU (Graphics Processing Unit): Deep Learning Backbone

• Design and Advantages: Originally used for graphics, modern GPUs have thousands of parallel cores designed for matrix/multi-vector operations, making them efficient in training and inferring deep neural networks.
• Performance examples:
  • NVIDIA RTX 3090: 10,496 CUDA core, up to 35.6 Tflops (Teraflops) FP32 calculations.
  • Recent NVIDIA GPUs include “tensor cores” for mixing precision, speeding up deep learning operations.
• Best for:
  • Training and inferring large-scale deep learning models (CNN, RNN, Transformers)
  • Typical batch processing in data centers and research environments
  • Supported by all major AI frameworks (Tensorflow, pytorch)

Benchmark: The 4X RTX A5000 setup can exceed a single expensive NVIDIA H100 in some workloads, balancing acquisition costs and performance.

NPU (Neural Processing Unit): Equipment AI Expert

• Design and Advantages: NPUs are ASICs (application-specific chips) designed for neural network operations. They optimize parallel, low-precision calculations for deep learning inference, often running edges and embedded devices at low power.
• Use cases and applications:
  • Mobile and Consumer: Power features such as Face Unlock, real-time image processing, language translation on devices such as Apple A-series, Samsung Exynos, Google Tensor chips, etc.
  • Edge and the Internet of Things: Low latency vision and voice recognition, smart city cameras, AR/VR and manufacturing sensors.
  • car: Real-time data from sensors for autonomous driving and advanced driver assistance.
• Performance examples: The Exynos 9820’s NPU speed is ~7 times faster than the predecessor of the AI mission.

efficiency: NPUs take precedence over raw throughput, extending battery life while supporting local advanced AI capabilities.

TPU (Tensor Processing Unit): Google’s AI Powerhouse

• Design and Advantages: TPU is a custom chip developed by Google, designed for large tensor computing, adjusting the hardware needs around frameworks such as TensorFlow.
• Key Specifications:
  • TPU V2: Up to 180 TFLOPS for neural network training and reasoning.
  • TPU V4: Available in Google Cloud, up to 275 TFLOPS per chip, scalable to “pods” of over 100 PETAFLOPS.
  • A dedicated matrix multiplication unit (“MXU”) for huge batch calculations.
  • Compared with contemporary GPUs and CPUs, the energy efficiency of reasoning is 30–80 times higher.
• Best for:
  • Training and serving large-scale models in the cloud (BERT, GPT-2, EFIDENENET)
  • High throughput, low latency AI for research and production pipelines
  • Tightly integrated with TensorFlow and Jax; increasingly interface with Pytorch

notes: TPU architecture is not as flexible as GPUs, and is optimized for AI, rather than graphics or general-purpose tasks.

Which models run where?

*CPU supports any model, but is not effective for large DNNs.

Data Processing Unit (DPU): Data Mover

• Role: DPU accelerates networking, storage and data movement, uninstalling these tasks from CPU/GPU. By ensuring that computing resources focus on model execution, rather than I/O or data orchestration, they can improve infrastructure efficiency in AI data centers.

Summary table: Technology comparison

Key Points

• CPU For general, flexible workloads, unrivaled.
• GPU Still the main force in training and running neural networks in all frameworks and environments, especially in all frameworks and environments outside of Google Cloud.
• npus Deliver real-time, privacy-protected and powerful AI for mobile and the edge, freeing local intelligence everywhere, from mobile phones to autonomous cars.
• TPU Provide unparalleled scale and speed for large models, especially in Google’s ecosystem, to integrate into the forefront of AI research and industrial deployment.

Choosing the right hardware depends on model size, computing requirements, development environment and required deployment (cloud vs. edge/mobile devices). A powerful AI stack often takes advantage of a mixture of these processors, each of which stands out.