Tensor Core
Specialized processing units within NVIDIA GPUs designed specifically for the matrix multiplication operations that dominate AI computation, delivering massive performance gains for AI workloads.
Tensor cores were introduced with NVIDIA's Volta architecture in 2017. The H100 contains 528 tensor cores capable of performing mixed-precision matrix operations at unprecedented speeds. The Blackwell B200 introduces FP4 tensor core operations, doubling throughput for inference workloads. Tensor cores can process matrix multiplications 8-16x faster than standard CUDA cores for AI workloads. They support multiple precision formats (FP64, TF32, FP16, INT8, FP4) allowing developers to trade precision for speed. Tensor cores are the key hardware innovation that made training trillion-parameter models feasible.
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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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