When NVIDIA launched its most powerful GPU, Tesla V100 Tensor Core, the substantial acceleration of AI, high-performance computing, data science, and graphics became unstoppable. Powered with the performance of as many as 100 CPUs, a single Tesla V100 GPU can largely cut down the time that ...
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The CUDA Fortran compiler from PGI now supports programming Tensor Cores with NVIDIA’s Volta V100 and Turing GPUs. This enables scientific programmers using Fortran to take advantage of FP16 matrix operations accelerated by Tensor Cores. Let’s take […] NVIDIA Expands its AI Edge Capabilities ...
OVHcloud and NVIDIA have partnered to deliver the best GPU acceleration platform, optimised for deep learning and high-performance computing. OVHcloud's NVIDIA GPU Cloud (NGC) combines the flexibility of the Public Cloud with the power of the NVIDIA Tesla V100 graphics card, providing a complete...
Use spectral normalization in the generator & discriminator. Uses two-time-scale update rule(TTUR) Uses hinge loss instead of using Least square loss. UNIT is trained using an NVIDIA DGX1 with 8 V100 32GB GPUs. You can try using fewer GPUs for training or you can reduce the batch size,...
2560 of its own predecessor, the TITAN V, which is based on the V100 MCM It lacks Quadro-like certifications for many Adobe and Autodesk applications (no serious studio would buy this over Quadro RTX 6000 even if the latter costs 2.5x more) The only area where it works ...