In this detailed overview, Harald Schäfer, CTO of comma.ai, advocates for companies to build and operate their own data centers rather than relying on cloud providers. He argues that self-hosting fosters better engineering incentives, provides greater control over infrastructure, and is significantly more cost-effective for consistent workloads like ML training—estimating a savings of $20M compared to cloud equivalents. The post details the technical architecture of comma's $5M facility, which features 600 GPUs across 75 in-house 'TinyBox Pro' machines, a custom outside-air cooling system, and 4PB of SSD storage. On the software side, comma utilizes tools like Slurm for workload management, PyTorch for training, and custom open-source solutions like 'minikeyvalue' for distributed storage and 'miniray' for task orchestration. This level of vertical integration allows for efficient model training and rapid code iteration within a streamlined, monorepo-based environment.
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