Interconnect
The high-speed networking technology that connects GPUs within and across servers in AI training clusters, where communication bandwidth critically determines training efficiency.
InfiniBand, produced by NVIDIA's Mellanox division, dominates AI cluster networking with 400 Gb/s per link, and 800 Gb/s (NDR) is deploying. NVIDIA's NVLink provides 900 GB/s bandwidth between GPUs within a single server. Training large models across thousands of GPUs requires constant synchronization, making interconnect bandwidth a critical bottleneck. Google uses custom-designed optical interconnects in its TPU pods. Ethernet-based alternatives are gaining ground for AI clusters, with Ultra Ethernet Consortium developing AI-optimized standards. Interconnect costs can represent 15-25% of total cluster investment.
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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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