NVIDIA A100
NVIDIA's previous-generation data center GPU for AI training and inference, which became the workhorse of AI infrastructure from 2020-2023 before being succeeded by the H100.
The A100, launched in May 2020, was built on the Ampere architecture and offered 80GB of HBM2e memory. It was the GPU that trained many of the models that launched the generative AI boom, including early GPT models and Stable Diffusion. A100 prices ranged from $10,000-15,000. US export controls restricted A100 sales to China in October 2022, making it a geopolitically significant chip. While superseded by the H100 and B200 for frontier training, millions of A100s remain in active service for inference and smaller-scale training workloads.
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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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