The Ultimate Guide to CPUs, GPUs, NPUs, and TPUs for AI/ML: Performance, Use Cases, and Key Differences

The burdens of artificial work and machine learning have fueled the development of specialized devices to largely accelerate the arithmetic of traditional central processing units. Each processing unit – CPU, GPU, NPU, TPU – plays a distinguished role in the ecosystem of Amnesty International, improved for specific models, applications or environments. Below is a technical collapse based on data for basic differences and best use cases.

CPU (CPU): Multi -use spine

• Design and strengths: CPUs are general purposes with a few strong cores-on the basis of a single-threading tasks and a variety of programs, including operating systems, databases, and the light inference of AI/ML.
• AI/ml role: The central processing unit can implement any type of artificial intelligence model, but lacks the massive parallel necessary to train or effectively deepens in deep learning.
• Best for:
  • Classic ml algorithms (for example, scikit-learn, xgboost)
  • Initial models and form development
  • Inference to small models or low productivity requirements

Technical note: For nervous network processes, the productivity of the CPU (usually measured in GFLOPS – a billion of floating point operations per second) is lagging behind specialized accelerators.

GPU (graphics processing unit): the backbone of deep learning

• Design and strengths: Originally for graphics, modern graphics processing units are characterized by thousands of parallel cores designed for multiple matrix/vectors, making them very effective in training and concluding deep nerve networks.
• Performance examples:
  • Nvidia RTX 3090: 10,496 Cuda Cores, up to 35.6 Tflops (Teraflops) FP32 Compute.
  • The last NVIDIA graphics processing units include “tensioner nuclei” for mixed accuracy and speeding deep learning processes.
• Best for:
  • Wide deep learning models (CNNS, RNNS, transformers)
  • Typical payment processing in the research center and research environments
  • With the support of all the frameworks of Amnesty International (Tensorflow, Pytorch)

Standards: The 4x RTX A5000 setting can exceed one NVIDIA H100 set and much more expensive in some work burdens, acquisition and performance costs.

NPU (neurological treatment unit): AI specialist on the device

• Design and strengths: NPUS is ASics (Special Chips App) Made for Neurological Network processes. It improves a low -resolution parallel account for deep learning inference, and is often operated with a low force of compact devices and compact devices.
• Using cases and applications:
  • Mobile and consumer: Run features such as opening the face, actual time processing, translating language on devices such as Apple A-Series, Samsung Exynos, and Google Tensor Chips.
  • Edge and Internet of ThingsLow vision of mandate and recognition, smart city cameras, AR/VR, and manufacturing sensors.
  • CarsReal -time data from independent driving sensors and advanced driver assistance.
• Example performance: NPU Exynos 9820 faster 7x than its predecessor for artificial intelligence tasks.

efficiency: NPUS gives energy efficiency priorities to raw productivity, which extends the battery life while supporting the locally advanced AI features.

TPU (tensioner processing unit): Google’s AI’s power

• Design and strengths: TPUS is dedicated chips developed by Google specifically for large tensioner accounts, and setting devices around the needs of frameworks such as Tensorflow.
• Main specifications:
  • TPU V2: up to 180 TFLOPs for nervous network training and inference.
  • TPU V4: Available in Google Cloud, up to 275 TFLOPS per slide, developed to “centuries” exceeding 100 Petaflops.
  • Specialized matrix reproduction units (“MXU” for huge payment accounts.
  • Up to 30-80X Energy Efficiency (Tops/Watt) for reasoning compared to contemporary graphics processing units and the CPU chain.
• Best for:
  • Training and service of huge models (BERT, GPT-2, efficiency) in the cloud on a large scale
  • High Productivity, Low Equity International for Research and Production Pipelines
  • Narrow integration with Tensorflow and Jax; Increasingly married with pytorch

Note: The TPU structure is less flexible than the GPU-converted to AI, not graphics or tasks for general purposes.

What are the models that do where?

*CPU supports any model, but they are not effective for large -scale DNNS.

Data processing units (DPU): Data engine

• role: DPUS units accelerate networks, storage and data movement, leading to emptying these tasks from the central processing units/graphics processing units. It enables the highest infrastructure efficiency in artificial intelligence data centers by ensuring focus on account resources on the implementation of the forms, not I/O or data format.

Summary table: technical comparison

Main meals

• Central processing units It is unparalleled for flexible work burdens for general purposes.
• Graphics processing units The backbone of the training and operation of nerve networks remains across all frameworks and environments, especially outside Google Cloud.
• Npus It dominates actual time, maintaining privacy, energy -saving AI for a mobile phone and edge, and open local intelligence everywhere from your phone to self -driving cars.
• Tpus It provides an unparalleled scale and speed for huge models – especially in the Google ecosystem – in introducing the boundaries of artificial intelligence and industrial publishing research.

The choice of appropriate devices depends on the size of the model, calculating requirements, the development environment, and the required publishing (Cloud VS. EDGE/Mobile). The strong AI staple a mixture of these processors, as it excels everywhere.