Dream Machine: Scaling to 1m users in 4 days — Keegan McCallum, Luma AI
Jul 19, 2025 · 19:03
Keegan McCallum, Head of ML Infrastructure at Luma AI, details how the company's Dream Machine model scaled from 500 to 9,000 H100 GPUs within hours to handle 1 million users in four days, outpacing ChatGPT's initial growth. He explains that their initial Triton inference server setup was brittle and ill-suited for multi-GPU, multi-node video models, prompting a re-architecture to a custom serving stack on vanilla PyTorch. To solve work starvation across user tiers, they implemented an SLO-based aging system that ranks jobs by the percentage of their worst-case wait time elapsed. For managing dozens of model versions, they store immutable full Python environments and checkpoints in object storage, with a YAML file controlling active deployments and enabling zero-downtime rollouts across thousands of GPUs. McCallum also discusses partnerships with Nvidia, AMD, and Grok, and how Luma's broader mission is to build general multimodal intelligence that generates, understands, and operates in the physical world.
