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Boosting NVIDIA MLPerf Training v1.1 Performance with Full Stack Optimization

In MLPerf training v1.1, we optimized across the entire stack including hardware, system software, libraries, and algorithms. 22 MIN READ
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MLPerf v1.0 Training Benchmarks: Insights into a Record-Setting NVIDIA Performance

Learn about some of the major optimizations made to the NVIDIA platform that contributed to the nearly 7x increase in performance since the first MLPerf training benchmark. 31 MIN READ
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Accelerating Scientific Applications in HPC Clusters with NVIDIA DPUs Using the MVAPICH2-DPU MPI Library

HPC and AI have driven supercomputers into wide commercial use as the primary data processing engines enabling research, scientific discoveries, and product development. These systems can carry complex simulations and unlock the new era of AI, where software writes software. 7 MIN READ
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Softbank Benchmarks vRAN with GPUs and the NVIDIA Aerial SDK

Virtualization is key to making networks flexible and data processing faster, better, and highly adaptive with network infrastructure from Core to RAN. 9 MIN READ
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Extending NVIDIA Performance Leadership with MLPerf Inference 1.0 Results

In this post, we step through some of these optimizations, including the use of Triton Inference Server and the A100 Multi-Instance GPU (MIG) feature. 7 MIN READ
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Doubling Network File System Performance with RDMA-Enabled Networking

Network File System (NFS) is a ubiquitous component of most modern clusters. It was initially designed as a work-group filesystem, making a central file store… 4 MIN READ