Lambda presents stable diffusion benchmarks with different GPUs including A100, RTX 3090, RTX A6000, RTX 3080, and RTX 8000, as well as various CPUs.
The best tools for monitoring your GPU usage and performance statistics compared.
While waiting for NVIDIA's next-generation consumer and professional GPUs, we decided to write a blog about the best GPU for Deep Learning currently available, as of March 2022.
NVIDIA® A40 GPUs are now available on Lambda Scalar servers. In this post, we benchmark the A40 with 48 GB of GDDR6 VRAM to assess its training performance using PyTorch and TensorFlow. We then compare it against the NVIDIA V100, RTX 8000, RTX 6000, and RTX 5000.
PyTorch and TensorFlow training speeds on models like ResNet-50, SSD, and Tacotron 2. Compare performance of the RTX 3090, 3080, A100, V100, and A6000 .
It’s important to take into account available space, power, cooling, and relative performance into account when deciding which RTX 3000 series cards to include in your next deep learning workstation.
Benchmarks for ResNet-152, Inception v3, Inception v4, VGG-16, AlexNet, SSD300, and ResNet-50 using the NVIDIA A100 GPU and DGX A100 server.
GPU benchmarks for training State of the Art (SOTA) deep learning models. We measure each GPU's performance by batch capacity and more.
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