Recent Blogs
Teamcenter on Azure with Oracle Exadata Database@Azure delivers seamless, low-latency connectivity between Teamcenter application tiers and Exadata within the same Azure region, combining Azure-nativ...
Jul 06, 2026
By Shantanu Patankar, and Azin Heidarshenas
As part of Azure's MLPerf Training v6.0 submission, we scaled Llama 3.1 405B (NVFP4) pretraining to 8,192 GB200 GPUs across 128 racks on Azure's Fairwa...
Jun 24, 2026
This blog presents a validation study of Siemens NX 2506 running in a multi-user Azure Virtual Desktop (AVD) environment powered by NVIDIA RTX PRO 6000 GPUs. It demonstrates how a single GPU-backed A...
Jun 17, 2026
Azure achieved the most performant MLPerf Training v6.0 result to date for Llama 3.1 405B, with a time-to-train of just over seven minutes according to MLCommons. This loadbearing benchmark measures ...
Jun 16, 2026
Co-Author:
Ansys/Synopsys
Roman Walsh
Madeleine Driver
Jun 11, 2026
10 MIN READ
The Paradigm Shift in Model Training
The conventional wisdom in deep learning has been simple: bigger models require bigger infrastructure. Training a 100-billion parameter language model tradition...
May 28, 2026
8 MIN READ
Every team that operates GPU clusters for AI has seen this pattern. The cluster boots, GPUs are visible, and scheduling works at a basic level. Then the first distributed training run stalls in NCCL ...
May 22, 2026
Standing up an N-node training or inference job and waiting forever for the model checkpoint to land on every node's NVMe? Here's a small Rust + MPI tool — azcp-cluster — that pays Azure egress once,...
May 06, 2026
By Valerie Cutts and Jithin Jose
Last fall we introduced Fairwater, the world’s most powerful AI datacenter. Delivering a system of this scale required rethinking how Azure designs supercompute...
May 06, 2026
3 MIN READ
Training large AI models on hundreds or thousands of nodes introduces a critical operational challenge: when a distributed job fails, quickly identifying the root cause across scattered logs can beco...
Mar 31, 2026