On-Premise AI
Running AI models on locally owned and operated hardware within an organization's own facilities, rather than using cloud-based services, typically chosen for data sovereignty, security, or latency reasons.
On-premise AI is preferred by organizations in regulated industries (healthcare, finance, defense) where data cannot leave secure environments. The market for on-premise AI infrastructure grew 35% in 2024 as enterprises deploy private GPU clusters. NVIDIA's DGX systems ($200,000-500,000 each) are purpose-built for on-premise AI. Running AI on-premise requires significant upfront capex and specialized staff but eliminates recurring cloud costs and data transfer concerns. Approximately 15-20% of enterprise AI workloads run on-premise, with the share growing as organizations scale beyond proof-of-concept deployments.
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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.
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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