Question text: How would you implement a custom distributed data loader for handling petabyte-scale datasets in a multi-GPU setup, ensuring minimal I/O bottlenecks and seamless integration with frameworks like PyTorch? Why it's asked: xAI's work on massive AI models for scientific breakthroughs requires efficient data pipelines; this evaluates deep expertise in scalable infrastructure to support training on universe-scale simulations without delays. Ideal answer traits: Outlines sharding strategies (e.g., TFRecord or WebDataset), incorporates asynchronous prefetching and caching, addresses fault tolerance with retries, and provides metrics like throughput gains or compatibility with xAI's cluster environments.