Why AI Needs Special Chips
AI workloads are fundamentally different from regular computing. Training and running AI requires massive parallel processing—multiplying billions of numbers simultaneously. Traditional CPUs aren't designed for this.
The GPU Boom
NVIDIA's stock rose over 1000% in 3 years because their GPUs are essential for AI. AI chip demand has become a geopolitical issue.
Types of AI Chips
GPUs (Graphics Processing Units)
- Originally designed for gaming graphics
- Perfect for AI: thousands of cores for parallel processing
- NVIDIA dominates (H100, A100, RTX series)
- AMD and Intel are catching up
TPUs (Tensor Processing Units)
- Google's custom AI chips
- Designed specifically for neural network math
- Powers Google Cloud AI services
Custom AI Accelerators
- Amazon Trainium/Inferentia — AWS AI chips
- Apple Neural Engine — In iPhones and Macs
- Cerebras — Wafer-scale chips
- Groq — Ultra-fast inference chips
NVIDIA's Dominance
- Controls ~80% of AI chip market
- CUDA software ecosystem locks in developers
- H100 GPUs cost $30,000+ and are in short supply
- Major AI labs spend billions on NVIDIA hardware
Training vs. Inference
- Training — Teaching the model (requires massive compute)
- Inference — Running the model for users (needs to be fast and efficient)
- Different chips optimized for each task
Geopolitics
- US restricts advanced chip exports to China
- Taiwan (TSMC) manufactures most advanced chips
- AI chip supply is a national security concern
- Countries racing to develop domestic chip capabilities
The Future
- More competitors challenging NVIDIA
- Specialized chips for different AI tasks
- AI chips in every device (phones, cars, appliances)
- Neuromorphic chips mimicking brain architecture
Summary
- • AI requires specialized chips for parallel processing
- • NVIDIA GPUs dominate; TPUs and custom chips compete
- • Chip access has become a geopolitical issue
- • AI chips are becoming ubiquitous in devices