You have a deep learning model running in your console or Jupyter notebook and would like to make it available to your company or deploy it to the world? AIME API is the easy and scalable solution to do so.
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.
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.
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.
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.
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.
A benchmark based performance comparison of the new PyTorch 2 with the well established PyTorch 1. The benchmarks cover different areas of deep learning, such as image classification and language models. It is shown that PyTorch 2 generally outperforms PyTorch 1 and is scaling well on multiple GPUs.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Deep learning applications require powerful multi-GPU systems for development and operation. Which infrastructure offers the best compromise between time-to-solution, cost-to-solution and availability of resources?
