As an NVIDIA Elite Partner, Exxact Corporation works closely with the NVIDIA team to ensure seamless factory development and support. We pride ourselves on providing value-added service standards unmatched by our competitors.
Multi-GPU Performance
Leverage the latest NVIDIA GPUs including the RTX 3090/3080/3070, TITAN RTX, Quadro RTX 8000/6000, and more to accelerate AI development.
Pre-Installed Frameworks
Our systems come pre-loaded with TensorFlow, PyTorch, Keras, Caffe, RAPIDS, Docker, Anaconda, MXnet and more upon request.
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.
Great for a single developer
Upgrade GPUs as you grow
Base Specs
- 1x Intel Core i9 Processor
- Up to 4x NVIDIA RTX 3090, RTX 3080, or RTX 3070 GPUs
- Up to 128GB System Memory
- 1x 2TB NVMe SSD (OS & Data)
Best price/performance configuration
Suitable for most AI projects
Base Specs
- 1x 3rd Gen AMD Ryzen Threadripper
- Up to 4x NVIDIA RTX 3090, RTX 3080, or RTX 3070 GPUs
- Up to 256GB System Memory
- 1x 2TB NVMe SSD (OS & Data)
Run multi-threaded applications easily
Train large models
Base Specs
- 2x Intel Xeon Scalable Processors
- Up to 4x NVIDIA RTX 3090, RTX 3080, or RTX 3070 GPUs
- Redundant Power Supplies
- 1x 1TB SSD (OS)
2x 2TB HDD (Data)
Not sure what you need?
Let us know what kind of project you have planned.
We can help you decide.
Tell us what you want to do.
EMLI: Pre-installed Developer-Ready Environment
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.
Find Your Perfect Deep Learning Environment with EMLI
| 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 |
Accelerating Deep Learning Initiatives with NVIDIA Powered Data Center Platforms
Training
With deep neural networks becoming more complex, training times have increased dramatically, resulting in lower productivity and higher costs. Exxact's deep learning infrastructure technology featuring NVIDIA GPUs significantly accelerate AI training, resulting in deeper insights in less time, significant cost savings, and faster time to ROI.
Explore Our Training Platforms
Inference
Data center managers must make tradeoffs between performance and efficiency. A single inference server from Exxact can replace multiple commodity CPU servers for deep learning inference applications and services, reducing energy requirements and delivering both acquisition and operational cost savings.
Explore Our Inference Platforms
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HGX-2 Benchmarks for Deep Learning in TensorFlow (16x V100)
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Hands-on TensorFlow Tutorial: Train ResNet-50 From Scratch Using the ImageNet Dataset
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Accelerating Epigenetic Research at UPenn’s Perelman School of Medicine with Exxact’s GPU Workstations
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The solution from Exxact allowed us to iterate quickly during the development of Snap It to the point where model development became interactive. We were so impressed with both the hardware and responsiveness of the support team that we recently made further investments with Exxact by purchasing a TensorEX 8-GPU turn-key deep learning solution.
Dr. Edward LoweDirector of Data Science
Lose It!
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