Scaling large AI models is less about raw compute and more about engineering precision. This session distills proven techniques for model and data parallelism drawn from real-world, production-scale deployments. We’ll cover how to partition massive models and datasets to maximize throughput, minimize communication overhead, and maintain accuracy under strict latency and cost constraints. You’ll see practical benchmarks, configuration patterns, and profiling results that reveal what actually works at multi-node, multi-GPU scale - along with the trade-offs that matter when the stakes are high. Whether you’re training in the cloud or optimizing inference pipelines, you’ll leave with an actionable framework for designing, evaluating, and deploying parallelized AI workflows that meet performance targets in production.