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IPMI Quickstart Guide
IPMI (Intelligent Platform Management Interface) is a standardized specification for out-of-band management and monitoring of computer hardware.
Deep Learning GPU Benchmarks
An overview of current high end GPUs and compute accelerators best for deep and machine learning and model inference tasks. Included are the latest offerings from NVIDIA: the Hopper and Blackwell GPU generation. Also the performance of multi GPU setups is evaluated.
PyTorch 2 GPU Performance Benchmarks (Update)
An overview of PyTorch performance on latest GPU models. The benchmarks cover training of LLMs and image classification. They show possible GPU performance improvements by using later PyTorch versions and features, compares the achievable GPU performance and scaling on multiple GPUs.
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AIME Machine Learning Framework Container Management
To set up and run a deep learning framework in a GPU environment, some prerequisites for installed drivers and libraries must be met. There are guides to get a specific version of your favourite framework up and running. But most easy is just to use the AIME MLC framework.
How to run and deploy Mixtral 8x7B and 8x22B
With Mixtral you get a collection of pretrained state-of-the-art MoE (Mixture of Experts) large language models for free. Unlike the well-known ChatGPT, Mixtral models are downloadable and compatible with existing hardware for execution.
How to run Llama 3 with AIME API to Deploy Conversational AI Solutions
With LLaMa 3 you get a collection of pretrained state-of-the-art large language models for free. Unlike the well-known ChatGPT, LLaMa models are downloadable and compatible with existing hardware for execution.
Deploy Stable Diffusion 3 with AIME API
Stable Diffusion 3 is a powerful deep learning model for text-to-image generation based on diffusion techniques, offering improved performance compared to its predecessors, including higher resolutions, better text generation, faster processing, and improved image compression.
Deep Learning GPU Benchmarks 2024
An overview of current high end GPUs and compute accelerators best for deep and machine learning tasks in 2024. Included are the latest offerings from NVIDIA: the Hopper and Ada Lovelace GPU generation. Also the performance of multi GPU setups is evaluated.
Deploy LLaMa 2 with AIME API Server for Operation of Conversational AI Solutions
LLaMa (short for "Large Language Model Meta AI") is a collection of pretrained state-of-the-art large language models, developed by Meta AI. Compared to the famous ChatGPT, the LLaMa models are available for download and can be run on available hardware.
How to run 30B/65B LLaMa-Chat on Multi-GPU Servers
LLaMa (short for "Large Language Model Meta AI") is a collection of pretrained state-of-the-art large language models, developed by Meta AI. Compared to the famous ChatGPT, the LLaMa models are available for download and can be run on available hardware.
Using Stable Diffusion with webUI in AIME MLC
If you're looking for a convenient and user-friendly way to interact with Stable Diffusion, the webUI from AUTOMATIC1111 is the way to go. This open-source project makes it easy to use image generation models and offers many other features in addition to the normal image generation.
Exploring Stable Diffusion with an AIME Workstation using AIME MLC
Using AI to generate images from text descriptions yields impressive results. Here you find an easy to follow description of installation and usage of the Stable Diffusion Txt2Img model.
Deep Learning GPU Benchmarks 2022
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 Hopper and Ada Lovelace GPU generation. Also the performance of multi GPU setups is evaluated.
Efficient deep learning: Training the ResNet50 model on the ImageNet data set
In order to achieve good results with the shortest possible training times when training deep learning models, it is essential to find suitable values for the training parameters such as learning rate and batch size.
Multi GPU training with Pytorch
Training deep learning models consist of a high amount of numerical calculations which can be performed to a great extent in parallel. Since GPUs offer far more cores than CPUs, GPUs (>10k cores) outperform CPUs (<= 64 cores) in most deep learning applications by factors.
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.
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.
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.
Deep Learning GPU Benchmarks 2019
A state of the art performance overview of high end GPUs used for Deep Learning in 2019. All tests are performed with the latest Tensorflow version 1.15 and optimized settings. Also the performance for multi GPU setups is evaluated.