GPU Cluster
A connected system of hundreds to tens of thousands of GPUs working together to train large AI models, linked by high-speed networking that enables coordinated parallel processing.
The largest GPU clusters have grown from hundreds of GPUs in 2020 to over 100,000 GPUs in 2025. xAI's Colossus cluster contains 100,000 NVIDIA H100 GPUs connected by high-speed InfiniBand networking. Meta operates clusters of 350,000+ GPUs across multiple data centers. Building a 10,000-GPU cluster costs approximately $500 million in hardware alone, plus facility and networking costs. Cluster efficiency depends critically on interconnect bandwidth — a 10% improvement in networking can improve training throughput by 20-30%. GPU cluster design has become a core competency for frontier AI labs.
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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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