What is the difference between NPU, GPU, and CPU, and their use in AI?

Difference Between NPU, GPU, and CPU in AI

These are three types of processing units with very different architectures and optimal use cases, especially for AI workloads.

Core Comparison

CPU (Central Processing Unit)

The general-purpose processor — good at sequential logic, branching, complex control flow.

pythonCopy
# CPU handles pre/post processing, control flow
import time

def cpu_preprocessing(texts: list[str]) -> list[str]:
    # String manipulation, parsing — CPU is great at this
    return [text.strip().lower() for text in texts]

# Model inference on CPU (slow for large models)
import torch
device = "cpu"  # Fallback if no GPU
model = model.to(device)
output = model(input_tensor.to(device))

AI Use Cases: Data preprocessing, simple models, inference for tiny models, control logic in agents.

GPU (Graphics Processing Unit)

Originally for rendering graphics (massively parallel pixel computation) — now dominant for AI because neural networks are also massively parallel matrix operations.

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import torch

# Check GPU availability
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using: {device}")

# Move model and data to GPU — massive speedup
model = model.to(device)
input_tensor = input_tensor.to(device)

# Matrix multiplication — GPU is 100-1000x faster than CPU
output = model(input_tensor)  # Runs on GPU

Key NVIDIA GPUs for AI:

NPU (Neural Processing Unit)

Purpose-built for neural network inference — found in modern phones, laptops, and edge devices.

Characteristics:

• Optimized specifically for matrix multiply-accumulate (MAC) operations
• Very power efficient (battery-friendly)
• Fixed pipeline (less flexible than GPU)
• Built into SoCs (Apple M-series, Qualcomm Snapdragon, Intel Core Ultra)

pythonCopy
# Running on Apple Silicon NPU (via Core ML)
import coremltools as ct
import numpy as np

# Convert model to Core ML (runs on Apple NPU)
model_coreml = ct.convert(
    pytorch_model,
    compute_units=ct.ComputeUnit.ALL  # Uses NPU when available
)

# Inference — automatically uses NPU on Apple Silicon
result = model_coreml.predict({"input": np.array(input_data)})

Examples:

• Apple M1/M2/M3/M4 — Neural Engine (up to 38 TOPS)
• Qualcomm Snapdragon 8 Gen 3 — Hexagon NPU (45 TOPS)
• Intel Core Ultra — AI Boost NPU
• Google Tensor — In Pixel phones

When to Use Each

Benchmark Example

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Run Llama 3.1 8B inference, generate 100 tokens:
  CPU (M2 Max, 96GB): ~8 tokens/sec
  GPU (RTX 4090):     ~80 tokens/sec
  NPU (Apple M3 Pro): ~15 tokens/sec