We understand every development environment is different, so shouldn't you have the option to choose what's best for you? All EMLI (Exxact Machine Learning Images) environments are available in the latest Ubuntu or CentOS Linux versions, and are built to perform right out of the box.
Our Customers
Built for Use Case
Build a custom solution specific for NLP, computer vision, generative models, reinforcement learning, or inference.
Wide Platform Selection
Exxact offers a wide selection of workstation and server platforms to meet the individual compute needs of each customer's unique use case.
Standard 3 Year Warranty
Have peace of mind, focus on what matters most, knowing your system is backed by a 3 year warranty and support.
Workstations & Servers for AI Development
AI Workstations
Develop and test modern AI models at your desktop.
AI Servers
Powerful enough for development teams to run larger complex models flawlessly.
AI Cluster Infrastructure
Tightly integrated HPC compute and storage infrastructure.
Not sure what your need?
Our system architects are here to help get your project going.
Accelerate Deep Learning Initiatives
NVIDIA DGX™ A100
The universal system for all AI workloads, offering unprecedented compute density, performance and flexibility in the world’s first 5 petaFLOPS AI system. Order yours today.
Exxact Machine Learning Images
Preinstalled Developer-Ready Environment
An EMLI Environment for Every Developer
| Most Popular | |||
Compare*Additional NGC (NVIDIA GPU Cloud) containers can be added upon request. |  Conda EMLISeparated Frameworks |  Container EMLIFlexible. Reconfigurable. |  DIY EMLISimple. Clean. Custom. |
|---|---|---|---|
Who is it for? | For developers who want pre-installed deep learning frameworks and their dependencies in separate Python environments installed natively on the system. | For developers who want pre-installed frameworks utilizing the latest NGC containers, GPU drivers, and libraries in ready to deploy DL environments with the flexibility of containerization. | For experienced developers who want a minimalist install to set up their own private deep learning repositories or custom builds of deep learning frameworks. |
Frameworks* | |||
| TensorFlow V1 | — | — | |
| TensorFlow V2 | — | ||
| PyTorch | — | ||
| MXnet | — | ||
| Caffe | — | — | |
| Caffe2 | — | ||
| Chainer | — | — | |
| Microsoft Cognitive Toolkit | — | — | |
Libraries* | |||
| NVIDIA cuDNN | |||
| NVIDIA Rapids | — | ||
| Keras | — | ||
| Theano | — | ||
| OpenCV | — | ||
Software Environments | |||
| NVIDIA CUDA Toolkit | |||
| NVIDIA CUDA Dev Toolkit | — | ||
| NVIDIA Digits | — | ||
| Anaconda | — | ||
Container Management | |||
| Docker | — | ||
Drivers | |||
| NVIDIA-qualified Driver | |||
Orchestration | |||
| Micro-K8s | Free upgrade available | Free upgrade available | Free upgrade available |
Great for Any Area of Research
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