Training Cluster
A large-scale system of interconnected AI accelerators (GPUs or TPUs) specifically configured for training large AI models, requiring high-bandwidth networking and massive power infrastructure.
Modern training clusters range from hundreds to over 100,000 accelerators. xAI's Colossus cluster contains 100,000 H100 GPUs and consumed an estimated 150 MW of power. Building a 10,000-GPU training cluster costs approximately $500 million in hardware, plus $100-200 million for facility and networking. Training clusters require 99.9%+ uptime — a single GPU failure in a multi-week training run can corrupt the entire process. Checkpoint saving, fault tolerance, and cluster management software have become critical engineering challenges as clusters scale beyond 10,000 GPUs.
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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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