Deep Learning GPU Benchmarks 2021

An overview of current high end GPUs and compute accelerators best for deep and machine learning tasks. Included are the latest offerings from NVIDIA: the Ampere GPU generation. Also the performance of multi GPU setups like a quad RTX 3090 configuration is evaluated. Overview of the benchmarked GPUs Although we only tested a small selection of all the available GPUs, we think we covered all GPUs that are currently best suited for deep learning training and development due to their compute and memory capabilities and their compatibility to current deep learning frameworks.

Convenient PyTorch and Tensorflow development on AIME GPU Servers

Modern AI development, especially when it comes to the training of Deep Learning models quickly reaches the performance limits of standard PC and notebook hardware. AIME GPU servers are the perfect solution with optimized multi GPU hardware for this task to enable fastest possible turnaround times. Development on a local machine is straight forward but how to use to the power of an AIME GPU server conveniently? To be able to run your code with the full performance of the AIME GPU Servers, you first need to get the code and data to the storage of the remote machine. The most basic way to achieve this, is to copy your finished code and data to the remote machine.

Deep Learning GPU Benchmarks 2020

An overview of current high end GPUs and compute accelerators best for deep and machine learning tasks. Included are the latest offerings from NVIDIA: the Ampere GPU generation. Also the performance of multi GPU setups like a quad RTX 3090 configuration is evaluated.

How to Setup a Remote Desktop Connection to an AIME-Server

A description of how to establish a Remote Desktop Connection to our AIME servers. The setup via command line as well as with several programs with graphical user interface for Linux, Windows and macOS is demonstrated.

AIME Machine Learning Framework Container Management

To install and set up a deep learning framework in a GPU environment can get quite tedious as the requirements for installed drivers and correct version of required libraries (CUDA, CUDNN) must be met to set it up successful. This guide describes how to install and set up a eligible version of Tensorflow, Pytorch or Mxnet in minutes and master the problem once and for all.

Deep Learning GPU Benchmarks 2019

A state of the art performance overview of current high end GPUs used for Deep Learning. All tests are performed with the latest Tensorflow version 1.15 and optimized settings. The results can differ from older benchmarks as latest Tensorflow versions have some new optimizations and show new trends to achieve best training performance and turn around times. Also the performance for multi GPU setups is evaluated.