Multi-Datacenter Training: OpenAI vs Google Infrastructure

Key Points

• Leading frontier AI model training clusters scaled to 100,000 GPUs in 2024
• 300,000+ GPU clusters in development for 2025 deployment
• Gigawatt clusters requiring advanced infrastructure with physical construction constraints
• OpenAI pursuing distributed multi-datacenter training to compete with Google's capabilities
• Stark infrastructure differences between Google's efficient design and competitors' approaches

Google vs Microsoft Infrastructure

• Google's AI training campus with 300MW+ power capacity expanding to 500MW next year
• Large cooling towers and centralized water systems rejecting ~200MW of heat
• Direct-to-chip liquid cooling with liquid-to-liquid heat exchangers for efficiency
• Microsoft's largest training cluster lacks liquid cooling, 35% lower IT capacity per building vs. Google

Key Insights

• Physical construction timelines limiting datacenter deployment expansion
• Liquid cooling critical for high-density GPU clustering and efficiency
• Telecom networking and long-haul fiber required for distributed infrastructure
• Hierarchical and asynchronous SGD enabling multi-datacenter training approaches
• Distributed infrastructure winners include networking and cooling solution providers

Source

• File: SA - Multi-Datacenter Training_ OpenAI's Ambitious Plan To Beat Google's Infrastructure.pdf
• Location: Dropbox/2. Semi/Datacenter/
• Pages: 38
• Date: September 4, 2024
• Authors: Dylan Patel, Daniel Nishball, Jeremie Eliahou Ontiveros
• Publisher: SemiAnalysis
• Type: Paid Research

Related

• _MOC-datacenter
• ai-datacenter-energy-dilemma
• liquid-cooling-opportunity-deepdive-isi