AI and machine learning workloads have driven a shift toward specialized silicon that outpaces traditional CPUs. In the current AI stack, CPUs, GPUs, NPUs, TPUs and DPUs each handle distinct workloads ranging from code execution to massive tensor math and data plane operations. Below is a concise, data-driven look at their architectures, performance and prime applications.
Central processing units (CPUs) serve as general-purpose engines with a small number of powerful cores. They excel at sequential logic, diverse software stacks and lightweight inference. Any AI model can run on a CPU, but GFLOPS counts trail those of dedicated accelerators. Common use cases include:
• Training classical ML tools such as scikit-learn or XGBoost.
• Prototype development and testing.
• Inference of compact models with low throughput demands.
A CPU’s breadth comes at the expense of parallelism needed for large-scale deep networks.
Graphics processing units (GPUs) evolved from rendering to host thousands of cores for matrix and vector operations. For instance, the NVIDIA RTX 3090 packs 10,496 CUDA cores and delivers up to 35.6 TFLOPS of FP32 performance. Tensor Cores on newer models accelerate mixed-precision deep learning in frameworks like TensorFlow and PyTorch. GPU clusters shine when training or serving large models—CNNs, RNNs or Transformers—in batch mode. A cluster of four RTX A5000 cards can exceed the performance of a single H100 at a fraction of the cost.
Neural processing units (NPUs) are ASICs tailored to neural network math, optimizing low-precision operations at low voltage. They power on-device AI features in smartphones—face unlock, real‐time imaging and live translation on Apple A-series, Samsung Exynos and Google Tensor chips. NPUs also drive vision, speech and sensor analytics in AR/VR headsets, smart cameras and industrial IoT, as well as sensor fusion for ADAS in vehicles. Samsung’s Exynos 9820 NPU runs tasks about seven times faster than its previous generation. Energy efficiency takes precedence over peak throughput, extending battery life in edge deployments.
Google’s tensor processing units (TPUs) focus on large-scale tensor math. TPU v2 delivers up to 180 TFLOPS for training and inference. TPU v4 on Google Cloud runs at 275 TFLOPS per chip, and multi-chip pods can exceed 100 petaFLOPS of aggregate compute. Specialized matrix multiplication units (MXUs) handle massive batches, and inference runs at 30–80× the TOPS per watt of comparable GPUs or CPUs. These units serve large models—BERT, GPT-2, EfficientNet—at scale, with built-in support for TensorFlow, JAX and growing PyTorch integration. Though powerful for AI, TPUs are not designed for graphics or general-purpose computing.
Data processing units (DPUs) offload network, storage and data movement duties from CPUs and GPUs. By handling packet inspection, encryption, storage management and other data-plane functions, DPUs let compute engines focus on model execution. In AI datacenters, this offloading boosts resource utilization and system throughput while reducing host CPU load.
CPUs remain the standard for general-purpose workloads and lightweight inference. GPUs are the preferred platform for training and running deep networks in research labs and cloud offerings outside Google’s hardware suite. NPUs excel in power-limited, on-device scenarios at the edge. TPUs deliver unmatched throughput and energy efficiency for large models within Google Cloud. DPUs boost datacenter efficiency by managing data plane duties. *General-purpose CPUs can run any AI model, but their lack of massive parallelism makes them inefficient for large-scale deep networks. Teams must weigh acquisition cost, power budget and software compatibility when assembling an AI environment. Deployment strategies often mix cloud-hosted accelerators with on-device NPUs and DPUs to satisfy diverse operational requirements across real-time and batch pipelines.