deep learning benchmarks gpu

When we put them together, we can observe a combined bandwidth of 360GB/s, clearly some competition between the CPU and GPU. Pre-ampere GPUs were benchmarked using TensorFlow 1.15.3, CUDA 10.0, cuDNN 7.6.5, NVIDIA driver 440.33, and Google's official model implementations. NVIDIA A100 Deep Learning Benchmarks for TensorFlow In future reviews, we will add more results to this data set. Jetson Benchmarks The same benchmark run on an RTX-2080 (fp32 13.5 TFLOPS) gives 6ms/step and 8ms/step when run on a GeForce GTX Titan X (fp32 6.7 TFLOPs). Performance Resnet50 (FP16) - 1 GPU NVIDIA Tesla V100 706.07 points NVIDIA Titan RTX … As we continue to innovate on our review format, we are now adding deep learning benchmarks. Deep Learning Bechmarking Suite - GitHub Pages For deep learning, which one between RTX

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