The evolution of processing chips has shaped the development of modern technology, enabling everything from basic tasks on mobile devices to the training of advanced artificial intelligence (AI) models. While CPUs (Central Processing Units) and GPUs (Graphics Processing Units) have been the traditional pillars of computing, the emergence of TPUs (Tensor Processing Units), specifically designed for AI workloads, has revolutionized efficiency and performance.
With the launch of Trillium, Google’s sixth generation of TPUs, AI computing reaches a new level, offering 4.7 times more performance than the previous version (TPU v5e) and a 67% increase in energy efficiency. But what makes these chips so different from one another, and why are TPUs changing the AI industry? Let’s find out.
1. What are the differences between CPU, GPU, and TPU?
Processors are integrated circuits designed to execute mathematical and logical operations, allowing devices to perform computational tasks. Although they all serve this function, they differ in their purpose and efficiency:
| Processor Type | Definition | Main Features | Use Example |
|---|---|---|---|
| CPU (Central Processing Unit) | Main processor that manages general operations of a system. | Versatile, can execute any type of task, but not the fastest option for massive workloads. | Computers, mobile phones, servers. |
| GPU (Graphics Processing Unit) | Processor optimized for graphic rendering and parallel calculations. | Highly efficient for parallelizable tasks, used in video games, video editing, and AI. | Graphics cards for gaming, 3D rendering, AI training. |
| TPU (Tensor Processing Unit) | AI-specialized chip designed by Google. | Designed for machine learning and deep learning calculations, ultra-efficient for AI workloads. | AI models in the cloud, machine learning applications. |
2. CPU: The foundation of general processing
The CPU, or Central Processing Unit, is the main brain of any electronic device. It was invented in the 1950s and has evolved constantly since then, increasing its power and efficiency.
Key Features:
✅ Versatility: Can run any type of software.
✅ Multiple cores: Modern processors include multiple cores to enhance multitasking performance.
✅ Sequential architecture: Executes tasks one after another, making it less efficient for massive calculations.
Where do we find CPUs?
- In personal computers, laptops, smartphones, and servers.
- They handle daily tasks such as opening applications, running operating systems, or browsing the web.
🔍 Example: When you open a spreadsheet, the CPU performs the necessary operations to display the data on the screen.
3. GPU: The revolution in graphic processing and AI
The GPU (Graphics Processing Unit) became popular in the 1990s with the rise of video games. Although its original purpose was to render graphics, its ability to execute multiple calculations in parallel has made it a fundamental pillar for AI.
Key Features:
✅ Parallel computing: Processes thousands of tasks simultaneously.
✅ Graphics optimization: Essential for gaming and 3D modeling.
✅ AI performance: Ideal for training deep neural networks.
Where do we find GPUs?
- In gaming PCs, workstations, video game consoles, and data centers.
- They are key in AI training, enabling models like ChatGPT, Gemini, and Copilot to analyze large volumes of data.
🔍 Example: An AI system like Stable Diffusion uses GPUs to generate images from textual descriptions in seconds.
4. TPU: Google’s bet on AI
In 2015, Google identified the need for a processor that was even more specialized than GPUs to accelerate the training and inference of AI models. This led to the creation of the TPU (Tensor Processing Unit), an ASIC (Application-Specific Integrated Circuit) designed for machine learning and deep learning.
Key Features:
✅ Designed for AI: Optimized to handle tensor calculations, the mathematical structure behind neural networks.
✅ Energy efficiency: Consumes less electricity than CPUs and GPUs for AI workloads.
✅ Massive performance: Allows Google to run models like Gemini, Bard, and algorithms from YouTube and Google Search.
Where do we find TPUs?
- Exclusively in Google Cloud data centers.
- Used by companies and developers to train large-scale AI models.
🔍 Example: Google uses TPUs to improve the accuracy of machine translation in Google Translate.
5. Trillium: The most advanced TPU to date
With the introduction of Trillium, the sixth generation of TPUs, Google has taken AI to a new level. This new chip, now available on Google Cloud, significantly enhances AI processing capability and efficiency.
What makes Trillium special?
✔ 4.7 times more powerful than its predecessor, TPU v5e.
✔ 67% more energy efficient, reducing environmental impact.
✔ Ability to handle larger and more complex models with lower latency.
The impact of Trillium on the industry
Google Cloud’s first customers are already using Trillium in:
- Medicine: RNA analysis for disease detection.
- Content generation: Creating videos from text in real-time.
- Business optimization: Massive data analysis in seconds.
🔍 Example: Startups and large companies can use Trillium to accelerate the training of AI models without needing to purchase expensive hardware.
Conclusion: The evolution of specialized computing
The difference between CPUs, GPUs, and TPUs lies in their specialization and efficiency. While CPUs are versatile and can handle any type of task, GPUs have revolutionized parallel processing and have become a key component in AI. However, TPUs represent the future of artificial intelligence, offering maximum performance with lower energy consumption.
With the launch of Trillium, Google Cloud strengthens its leadership in AI infrastructure, allowing companies and developers to access one of the most advanced platforms in the world. As artificial intelligence continues to evolve, the demand for specialized chips will grow, taking computing to new levels of efficiency, speed, and sustainability. 🚀