Artificial Intelligence Machines

Built for Deep Learning

Workstations & Rack Server

Save up to 90% by switching from your cloud provider to AIME products. Our multi-GPU accelerated computers are shipped with preinstalled frameworks like Tensorflow, Keras, PyTorch and more. Start training right out of the box.

Configure

Features

Optimized for Deep Learning

Our machines are designed and built to perform on deep learning applications.

Deep learning applications require fast memory, high interconnectivity and lots of processing power. Our 4-GPU design reaches the hightest currently possible throughput within this form factor.

Well Balanced Components

All of our components have been selected for their energy efficiency, durability and high performance. They are perfectly balanced, so there are no performance bottlenecks. We optimize our hardware in terms of cost per performance, without compromising endurance and reliability.

Tested with Real Life Deep Learning Applications

Our hardware was first designed for our own deep learning application needs and evolved in years of experience in deep learning frameworks and customized PC hardware building.

Benefits for your Deep Learning Projects

Iterate Faster

To wait unproductively for a result is frustrating. The maximum waiting time acceptable is to have the machine work overnight so you can check the results the next morning and keep on working.

Extend Model Complexity

In case you have to limit your models because of processing time, you for sure don't have enough processing time. Unleash the extra possible accuracy to see where the real limits are.

Train with more data, learn faster what works and what does not with the ability to make full iterations, every time.

Explore Without Regrets

Errors happen as part of the develoment process. They are necessary to learn and refine. It is annoying when every mistake is measurable as a certain amount of money, lost to external service plans. Make yourself free from running against the cost counter and run your own machine without loosing performance!

Protect Your Data

Are you working with sensitive data or data that only is allowed to be processed inside your company? Protect you data by not needing to upload it to cloud service providers, instead process them on your in-house hardware.

Start Out Of The Box

Our machines come with preinstalled Linux OS configured with latest drivers and frameworks like Tensorflow, Keras, PyTorch and MXNet. Just login and start right away with your favourite Deep Learning framework.

Save Money

Cloud services which offer a comparable performance have an hourly price rate of €14 or even more. These costs can quickly grow to hundreds of thousands of Euro per year just for a single instance.
Our hardware is available for a fraction of this cost and offers the same performance as Cloud Services. The TCO is very competitive and can save you service costs in the thousands of Euro every month.

Read more: CLOUD VS. ON-PREMISE - Total Cost of Ownership Analysis

Our Products

AIME R400 Rack Server

Your Deep Learning server. Built to perform 24/7 at your inhouse data center or co-location.

  • GPU: 4x NVIDIA RTX 2080TI or
    4x NVIDIA RTX Titan or
    4x NVIDIA Tesla V100
  • CPU: AMD Threadripper 12-32 Cores
  • RAM: 64 - 128 GB, optional ECC
  • SSD: 1-4 TB, NVMe (QLC/TLC/MLC)

AIME T400 Workstation

The perfect workstation for Deep Learning development. Own the power of 4 GPUs directly under your desk. Its liquid-cooling whisper-silent technology makes it suitable for use in office environments.

  • GPU: 2-4x NVIDIA RTX 2080TI
  • CPU: AMD Threadripper 12-32 Cores
  • RAM: 64 - 128 GB, optional ECC
  • SSD: 1-4 TB, NVMe (QLC/TLC/MLC)

Are you missing your configuration? We also serve custom made solutions. We have machine learning hardware solutions for:

  • Development Teams
  • Inhouse Company Services
  • Data Centers
  • Cloud Hosting

Contact us

Call us or send us an email if you have any questions or if you want to purchase one of our systems in advance before our webshop is online.