NVIDIA AI Enterprise is a comprehensive suite of artificial intelligence and data analytics software,
that is optimized, certified and supported by NVIDIA to run on VMware vSphere alongside NVIDIA-Certified Systems.
It includes technologies for rapid deployment, management and scaling of AI computing in the modern hybrid cloud.
NVIDIA AI Enterprise is a comprehensive enterprise platform optimized for AI computing. This integrated platform provides a best-in-class AI software suite – NVIDIA AI Enterprise Suite, optimized and exclusively certified for the industry’s leading virtualization platform – VMware vSphere.
This platform, which can only run on NVIDIA-Certified Systems, significantly accelerates the pace at which developers can develop AI applications, while enabling organizations to scale modern computing workloads on the same VMware vSphere infrastructure they have already invested in.
Accelerated systems with Nvidia GPUs and network elements
Hypervisor for management and orchestration of virtualized environments
Software for infrastructure optimization
Drivers and tools for running containers
Frameworks and tools for AI
Premium NVIDIA Enterprise Support covers the entire platform
Optimised for high performancePerformance comparable to bare-metal deployments across multiple nodes, suitable for large and complex computational workloads for training AI models and machine learning.
Certified for VMware vSphereReduce the risks of deploying AI in your infrastructure with a complete suite of NVIDIA AI Enterprise software, a VMware vSphere-certified data centre platform.
NVIDIA Enterprise SupportTake advantage of industry-leading NVIDIA Enterprise Support to keep critical projects and processes running reliably and without interruption.
VMware and NVIDIA have teamed up to make it easier to deploy AI in every enterprise and industry.
NVIDIA AI Enterprise is a suite of AI and data science applications and frameworks optimized and certified by NVIDIA exclusively for running on VMware vSphere with NVIDIA-certified accelerated systems. It includes key technologies and software for rapidly deploying, managing, and scaling AI compute workloads in the modern hybrid cloud.
The NVIDIA AI Enterprise software suite, which is optimized for VMware vSphere environments, delivers performance nearly comparable to bare-metal deployments, even across multiple nodes.
GPU virtualization with NVIDIA AI Enterprise does not limit the efficiency and performance of accelerators, significantly reducing the time required to develop new AI applications and services.
*Multi-node scaling requires an Ethernet network card that supports RoCE. For best performance, NVIDIA recommends using a Mellanox ConnectX-6 Dx network adapter and an NVIDIA H200 GPU in each virtual system used for multi-node scaling.
Many AI experts are trying to scale computing resources to reduce the time it takes to complete neural network training or achieve real-time results. Using a multi-GPU approach brings researchers closer to achieving their goals because they can experiment with different neural networks and algorithms more quickly. As scientists train more and more models, the size and consumption of data can grow significantly.
The deployment of virtual GPUs(vGPUs) for deep learning computing can be designed using three different approaches in a virtualized environment:
Computational models can be small enough to run on one or more GPUs within a server, but as datasets grow, training times increase.
This is when it is appropriate to use distributed training with multiple nodes. The goal is to create a model using large datasets that understand patterns and relationships between data, rather than just the data itself.
This requires fast data exchange between multiple nodes during the computational process, and GPUDirect RDMA together with ATS provides a high-performance network connection between these nodes.
Each organization should evaluate its needs and choose the right approach to build its infrastructure for Deep Learning computing.
As you can see in the figure opposite, starting at the top left, data scientists must first prepare the data before they can train the neural network. To this end, RAPIDS is a great tool for machine learning computations, as well as for formatting and labeling the data that will be used for training computations.
Once the data is ready, the AI expert moves on to training. NVIDIA AI Enterprise offers prebuilt and tuned containers for training neural networks using tools such as TensorFlow and PyTorch.
The data scientist can further optimize their newly trained models using the TensorRT SDK and other NVIDIA tools to make them as efficient as possible. This is done by merging neural layers and eliminating redundant steps.
Finally, once the model is ready for production, the NVIDIA Triton Inference Server can handle incoming inference requests. Available as a Docker container, Triton Inference Server supports both GPU and CPU computation. It allows front-end client applications to send cluster inference requests for AI and can manage models from cloud storage of those models as well.
This is one of the most often questions we can hear from our customers. There are a couple of techniques you can use as described in a webinar on the Multi-GPU topic. Or you can attend Fundamentals of Deep Learning for Multi-GPUs workshop organized by us, together with NVIDIA Deep Learning Institute (DLI).
Frameworks for AI and data science are delivered as image containers. These containers can be run, for example, using Docker.
One of the many advantages of using containers is that your application, bindings, and environment variables are installed once into the container image; rather than on the system you are running on. In addition, the main benefits of using containers also include:
During the webinar, we will present news from the world of artificial intelligence, seupercomputing and visualization announced at NVIDIA GTC22. And because this year’s conference was packed with technology (e.g. the new Hopper GPU architecture), we will present a summary of the highlights.
In the second part of the webinar, we will show how to quickly and efficiently deploy AI applications, frameworks and libraries in a VMware environment. What are the NVIDIA AI Enterprise solutions and its deployment on certified Lenovo servers? You can also look forward to the issue of sharing NVIDIA GPU accelerators across multiple users, while we will also introduce the possibilities of connecting your infrastructure to the NVIDIA GPU Cloud (NGC) – a catalog of optimized AI containers or pre-trained models.
We have a wide range of NVIDIA GPU cards available for performance testing and heavy-duty processing.
If you are interested in our testing offer, please fill out this form.
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