GPU Shortage
The persistent global undersupply of high-end AI accelerator chips, primarily NVIDIA GPUs, driven by explosive demand growth outpacing semiconductor manufacturing capacity.
Wait times for NVIDIA H100 GPUs exceeded 36 weeks at peak demand in 2023-2024. The shortage has driven GPU prices 2-3x above list price on secondary markets. TSMC, which manufactures NVIDIA's chips, has allocated increasing capacity to AI accelerators but cannot expand fabrication plants fast enough. The shortage is one reason Big Tech companies are investing over $650 billion in AI infrastructure. It has also accelerated development of alternative chips by AMD, Intel, Google (TPUs), Amazon (Trainium), and startups like Cerebras and Groq.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
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