Audience: Platform engineers, ML engineers, and developers who need a small, understandable GPU development cluster without the abstraction of a managed Kubernetes service. Reference topology: ...

By Mark Gitau and Azin Heidarshenas In the MLPerf Inference v6.1 round, Azure demonstrated strong Deepseek-R1 performance on GB300 NVL72 and GB200 NVL72 platforms. Azure submitted GB200 results at ...

In large scale AI clusters storage is responsible for delivering training data fast enough to keep accelerators busy and completing checkpoint saves and recoveries quickly enough to avoid costly stal...

Overview Microsoft HPC Pack is a Windows‑based high‑performance computing (HPC) scheduler that enables customers to deploy, manage, and operate HPC workloads in on‑premises and hybrid environments....

Introduction Traditional High-Performance Computing (HPC) is central to most scientific computing and engineering applications. These systems are based on well tested, repeatable, and scalable comp...

Introduction High-performance computing is the engine behind the most demanding engineering breakthroughs. In electronic design automation, HPC enables massive simulation farms, verification regres...

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...

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 ...