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Fluid compute: Evolving serverless for AI workloads

Serverless was built for the web. AI just broke it.

Reporting by Vercel Blog 1 min brief
Paper-cut illustration of an amber microchip with circuit paths extending into a row of data-center cabinets.
The infrastructure powering AI.AI illustration by KeyNews
The KeyNews take

Why it matters

Traditional serverless infrastructure wasn't designed for LLM workloads' sustained compute and continuous execution demands. Vercel's 'fluid compute' signals a fundamental shift in how deployment platforms must architect for AI—a challenge every infrastructure vendor will face.

The key facts

8 to know

  1. Traditional serverless designed for stateless, transactional web apps

  2. LLM interactions require sustained compute and continuous execution patterns

  3. Vercel introducing 'fluid compute' as evolution of serverless for AI

  4. Infrastructure architecture shift driven by AI workload requirements

  5. Serverless computing redesigned for AI workloads

  6. LLM interactions require sustained compute vs. stateless transactions

  7. Continuous execution patterns needed for production AI

  8. Infrastructure gap between web app and AI app requirements

Go to the source

Vercel Blogvercel.com

Publisher excerpt: AI’s rapid evolution is reshaping the tech industry and app development. Traditional serverless computing was designed for quick, stateless web app transactions. LLM interactions require a different sustained compute and continuous execution patterns.
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