Cloud Computing for AI
The delivery of AI computing resources — GPU access, pre-trained models, and managed ML services — over the internet, enabling organizations to use AI without owning hardware.
The cloud AI market exceeds $80 billion annually, with AWS, Azure, and Google Cloud commanding over 65% share. Cloud providers offer GPU instances starting at $1-3 per hour, making AI accessible to startups and researchers. Specialized AI cloud providers like CoreWeave and Lambda are growing rapidly. Cloud AI services range from raw GPU rental to fully managed AI platforms with pre-built models. The cloud model shifts AI costs from capex to opex, reducing barriers to entry. Over 90% of AI workloads run in the cloud, though on-premise and edge deployment is growing.
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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.
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.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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