Training efficiency starts with precision. This session explores Azure supercomputing validation—from GPU kernels to LLAMA pretraining and large-scale model training. The process detects bottlenecks early, reduces cost, and boosts performance. Customers gain predictable throughput, faster training, and confidence in Azure’s readiness for multi-billion parameter models. Attendees will gain practical insights and engage directly with the engineers driving these innovations.

Azure sets new inference records with 865K and 1.1M tokens/sec on ND GB200/GB300 v6 VMs. These results stem from deep stack optimization—from GPU kernels like GEMM and attention to multi-node scaling. Using LLAMA benchmarks, we’ll show how model architecture and hardware codesign drive throughput and efficiency. Customers benefit from faster time-to-value, lower cost per token, and production-ready infrastructure. Attendees can connect with Azure engineers to discuss best practices.