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Definition of GPU (Graphics Processing Unit)
A GPU (Graphics Processing Unit) is a specialized processor designed to carry out high -speed parallel calculations. Initially developed for graphic rendering, it has become A key element of calculation in artificial intelligenceespecially for training and inference of models.
Why is the GPU essential in AI?
- Optimized for parallel calculation : he can execute Thousands of operations simultaneouslywhich accelerates the treatment of neural networks.
- Indispensable for LLMS training : Openai, Google and Meta use Clusters of thousands of GPUs To train their models.
- Until 100 times faster than CPUs For some AI tasks.
GPU market leaders
- NVIDIA H100 : absolute reference for IA training, but costs between 30,000 and 40,000 dollars unity.
- AMD Mi300X : rising alternative against NVIDIA, optimized for supercomputers.
- Intel Gaudi 3 : Designed for AI, promises increased performance at lower cost.
Technological issues
1️⃣ High shortage and price 💰
- Demand exceeds supply, making the H100 almost not found on the market.
- Companies rent GPUs at gold prices on Azure, AWS and Google Cloud.
2️⃣ Emerging competition 🏗️
- Google (TPU), AWS (Trainium), Cerebras and Groq offer Specialized alternatives.
- China develops its own chips to reduce its dependence on American GPUs.
3️⃣ Energy efficiency 🌍
- A cluster of 10,000 GPU consumes the equivalent of a small town.
- Innovations like the watercooling And software optimization is trying to reduce this impact.
The future of GPUs in AI
✅ Arrival of new architectures more energy efficient.
✅ Diversification of suppliers to reduce dependence on Nvidia.
✅ Optimization of models to use less GPU while maintaining good performance.
