GPU (Graphics Processing Unit)
A specialized processor originally designed for rendering graphics, now the primary hardware used for training and running AI models due to its parallel processing capabilities.
NVIDIA commands approximately 64% of the AI chip market, which generated $66.2 billion in 2024. A single NVIDIA H100 GPU costs $25,000–40,000. An estimated 12 million+ AI GPUs are deployed globally, consuming over 120 million GPU-hours daily. NVIDIA's data center revenue grew 300% year-over-year in 2024. US export controls on advanced AI chips to China have made GPU access a geopolitical strategic resource.
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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.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
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.
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