AI chip
A specialized processor (GPU, TPU, NPU) optimized for training and inferencing neural networks—faster and cheaper than CPUs for AI.
Think of it like
A engine built specifically for speed; a car engine, not a truck engine.
Example
NVIDIA's H100, Google's TPU, Apple's Neural Engine. Each optimized for matrix operations that power neural nets.
How it actually works
AI chips are the bottleneck. Demand >> supply. NVIDIA dominates (80%+ market share). Alternatives exist (TPU, Cerebras, Graphcore) but are niche or closed. Everyone wants to design their own chip for cost/advantage; success is rare.
For product teams
Chip availability and cost directly limit model size and deployment scale.
For engineers
Matrix-optimized; high bandwidth memory; interconnect is critical for distributed training.
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