Modern artificial intelligences wouldn’t exist without powerful hardware.
Every time an AI model generates text, an image, or sound, millions of calculations are executed in parallel by GPUs and neural processors. GPUs (Graphics Processing Units), originally created for videogames, are now the core of machine learning thanks to their ability to process huge amounts of data at the same time.

💡 In just one second, a GPU can perform billions of mathematical operations, allowing AI models to “think” faster than any human brain.

🟩 NVIDIA

The undisputed leader in the sector: its H100 and B200 Blackwell GPUs are the foundation of almost all AI data centers worldwide.

NVIDIA is considered “the company that powers artificial intelligence,” and today it is worth over $2,000 billion. 

🟥 Google TPU

Tensor Processing Units are Google’s proprietary chips, optimized for training its own models (Gemini, PaLM 2).

They deliver exceptional performance with lower power consumption. 

🟪 Apple Neural Engine

Built into iPhone and Mac, the Neural Engine enables machine-learning models to run directly on the device, without a cloud connection: a step toward local and private AI. 

🟧 AMD e Intel

They compete with solutions dedicated to the cloud and enterprise sector.
AMD, in particular, with the MI300X series, aims to challenge NVIDIA in efficiency and power.

After GPUs and neural chips, the next revolution is already underway:

• Quantum chips → leverage the principles of quantum mechanics to process multiple logical states at the same time, accelerating model training.
  
  

• Neuromorphic chips → mimic the structure of the human brain, enabling more efficient processing and …
  
  

• Edge AI → mini AI processors embedded in portable devices, cars, and sensors, capable of processing data in real time without the cloud.
  
  

“In the future, every object could have its own built-in artificial intelligence, even without an internet connection.”

Companies are investing billions to secure resources and supplies.
In 2025, demand for AI GPUs surpassed global production capacity, triggering a true silicon crunch.

This has led to:

• higher server prices,
  
  

• a concentration of technological power in the hands of a few,
  
  

• and the rise of startups developing open-source AI chips to decentralize computing power.

• ChatGPT-5 – OpenAI’s next big leap 

• Gemini – Google DeepMind’s multimodal intelligence 

• L’era degli agenti AI – Intelligences that work on their own 

• NVIDIA – The Future of AI Chips (YouTube)

• Google Cloud – Inside TPU Technology (YouTube)

• IBM Research – Neuromorphic Computing Explained