It is 2025, and we have officially entered the age of artificial intelligence. After years of research and development, humans have created AI systems that can help find information faster, automate repetitive tasks, accelerate medical research, and make learning technology more accessible to everyone.
Although we are now in the AI era, the full potential of AI and its impact on the world remain uncertain. Industry analysts anticipate that robots may soon walk the streets, science‑fiction‑style vehicles may appear, and futuristic devices that do not yet exist could become a reality.
When discussing AI, the focus is often on software—the algorithms and models. Equally important is the hardware, the “brain” of AI, which includes the chips that perform the processing behind the scenes. Software intelligence depends entirely on hardware intelligence.
This article highlights the primary types of chips that drive AI: CPU, GPU, TPU, and LPU. Whether the reader is a developer, a technology enthusiast, or simply curious about the future, understanding these chips is essential because they are shaping the technology of tomorrow.
Chip Comparison: CPU, GPU, TPU, LPU
| Feature | CPU | GPU | TPU | LPU |
|---|---|---|---|---|
| Full Name | Central Processing Unit | Graphics Processing Unit | Tensor Processing Unit | Language Processing Unit |
| Primary Function | General-purpose computing | Parallel processing and graphics | AI/ML acceleration | Language model inference |
| Architecture | Few complex cores (2–64) | Thousands of simple cores | Matrix multiplication units | Deterministic execution core |
| Processing Style | Sequential tasks | Massively parallel | Matrix operations | Sequential token processing |
| Best For AI Training | Poor | Excellent (Industry standard) | Good (Google ecosystem) | Not designed for |
| Best For AI Inference | Basic models only | Excellent and versatile | Excellent and efficient | Revolutionary for LLMs |
| Power Consumption | Moderate (15–250W) | Very high (250–700W+) | High but efficient | Highly efficient |
| Cost (Data Center) | $500 – $10,000+ | $10,000 – $40,000+ | Cloud service only | Inference API service |
| Key Strength | Versatility and quick decisions | Massive parallel throughput | AI inference efficiency | Ultra-low latency for text |
| Key Weakness | Poor parallel performance | High power and cost | Limited flexibility | Hyper-specialized |
| Memory Approach | Hierarchical cache | GDDR/HBM memory | On-chip memory | Massive single-thread bandwidth |
| Programming | C++, Python, Java | CUDA, OpenCL | TensorFlow | Groq API |
| Leading Examples | Intel Core, AMD Ryzen, Apple M-series | NVIDIA A100/H100, RTX series | Google TPU v4/v5 | GroqChip LPU-1 |
| Use Case Example | Running operating system, web browser | Training AI models, gaming | Google Search, Translate | AI chatbots, text generation |
Which Chip is For You?
Everyday User: You have a CPU (and often a small built-in GPU) in your laptop and phone. It's perfect for web browsing, emails, and documents. You don't need to think about the rest!
Gamer or Video Editor: You need a powerful GPU (like from NVIDIA or AMD) in your computer. It will render your games and videos beautifully.
Company Training a New AI Model: You will buy or rent thousands of GPUs (like NVIDIA's H100). They are the most versatile for the heavy lifting of training.
Company Running a Chatbot Service (like ChatGPT): You have two great choices:
- Use GPUs (versatile and powerful).
- Use LPUs (if your main goal is raw, unbeatable speed and responsiveness for text generation).
Large-Scale Company like Google: Running its services requires using their own TPUs by the millions to power search, photos, and translate because it's hyper-efficient for their specific, massive-scale needs.
If you do not understand the terms in the table above, do not worry. In my next blog, I will explain each term so that you can understand the technical details. Until then, happy reading.
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