Edge AI
Running AI models directly on local devices — smartphones, IoT sensors, autonomous vehicles — rather than in the cloud, enabling real-time processing, privacy preservation, and offline operation.
The edge AI market is projected to reach $39 billion by 2026, driven by demand for real-time inference in autonomous vehicles, industrial IoT, and mobile devices. Apple's Neural Engine processes 35 trillion operations per second on iPhone. Qualcomm's AI engine powers on-device AI across 2+ billion Android devices. Edge AI eliminates latency (critical for autonomous driving), preserves privacy (data never leaves the device), and reduces cloud costs. Model compression techniques like quantization and distillation are essential for fitting powerful models onto resource-constrained edge devices.
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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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