ASIC (Application-Specific Integrated Circuit)
A custom-designed chip optimized for a specific AI workload, offering superior performance and energy efficiency compared to general-purpose processors for that particular task.
Google's TPU, Amazon's Trainium, and Microsoft's Maia are all ASICs designed for AI training and inference. ASICs can be 10-100x more energy efficient than general-purpose GPUs for their target workloads. Google has deployed over 1 million TPUs across its data centers. The trade-off is flexibility — ASICs cannot be reprogrammed for different tasks like GPUs can. The AI ASIC market is growing as companies seek to reduce dependence on NVIDIA and optimize for their specific model architectures. Custom ASICs are particularly attractive for inference at scale.
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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.
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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