Meta aims to double its GPU count to 1.3M for AI development

Meta CEO Mark Zuckerberg plans to significantly expand the company’s AI computing capabilities by doubling its GPU count to 1.3 million units by the end of the year.

Strategic objectives: Meta aims to develop cutting-edge AI assistants and launch its Llama 4 model to compete with industry leaders ChatGPT and Google Gemini.

• Zuckerberg expects Meta AI to serve over 1 billion users in 2025
• The company plans to create an “AI engineer” capable of contributing to Meta’s R&D efforts
• This expansion follows recent layoffs of 3,600 Meta employees

Infrastructure investment: Meta is planning a massive data center expansion that will require unprecedented power and space requirements.

• The facility would occupy an area comparable to “a significant part of Manhattan”
• The data center will consume 2 gigawatts of power, far exceeding the 30 megawatts used by America’s fastest supercomputer
• Meta plans to invest up to $65 billion in capital expenditures this year for the data center build-out

Competitive landscape: Major tech companies are racing to build massive AI infrastructure.

• Elon Musk is expanding his Memphis supercomputer from 200,000 to 1 million GPUs
• Sam Altman recently announced Project Stargate, a $500 billion collaboration with Softbank and Oracle for AI data centers
• Microsoft plans to spend $80 billion on AI-enabled data centers in FY 2025
• Microsoft is restarting a nuclear facility at Three Mile Island to meet AI power demands

Energy implications: The scale of power consumption for next-generation AI infrastructure presents unprecedented challenges.

• OpenAI envisions future data centers requiring 5 gigawatts of electricity
• This power requirement exceeds the output of five typical nuclear power plants
• The increasing energy demands highlight the environmental and infrastructure challenges facing AI development

Looking ahead: The massive infrastructure investments by Meta and its competitors signal a fundamental shift in computing resources required for advanced AI development, raising questions about energy sustainability and the concentration of AI capabilities among a small number of powerful tech companies.