Serverless GPU: Deploy AI Models in Seconds, Not Hours
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Serverless GPU computing lets developers run AI inference workloads without managing infrastructure, paying only for actual compute seconds used. RunPod's new Flash product simplifies this further by letting developers deploy GPU-backed Python functions using a simple decorator — no Dockerfile, no web console, no handler signature boilerplate. The video walks through building a two-endpoint AI agent: a LangGraph orchestrator on a cheap CPU worker that calls an LLM endpoint running Qwen 2.5 on an RTX 4090. Flash handles packaging, provisioning, dependency installation, and teardown automatically. RunPod supports 30+ GPU types (from 4090s to H100s and B200s), per-second billing, and on-demand multi-GPU clusters. Caveats include cold starts of 30–90 seconds for first LLM calls and no HIPAA compliance yet.