Empower Your AI Workloads with Azure's Virtual Machines

Table of Contents:

1. Introduction

2. Azure's Infrastructure Strategy

   • Overview of Virtual Machines
   • Different Kinds of Accelerators
   • Optimization for Various Workloads

3. User Profiles in the Visualization Market

   • Different Levels of Users and Their Requirements

4. Azure's Offerings for Remote Visualization

   • GPU Requirements for Different User Profiles
   • Benefits of Moving to the Cloud

5. Introduction to Azure's AI Virtual Machines

   • Different Types of VMs for AI Workloads
   • Offering Versatility and Scale

6. NV A10 v5: A New GPU-Enabled Virtual Machine

   • Overview of the NV A10 v5 Series
   • GPU Partitioning and Memory Capacities
   • High-Frequency CPU Performance

7. NC and ND Series: Roadmap for AI Virtual Machines

   • Introduction to the NC and ND Series
   • NDm A100 v4: High-End Distributed AI Training Platform
   • NCA 100 v4: Mid-Range AI Training GPU (Upcoming Release)
   • NC8ads v4: Future Inferencing and Light Compute Platform

8. Software Platform Features for Azure's AI Virtual Machines

   • Value of Software Tier for Operation and Management
   • Additional Offerings to Enhance Workload Management

9. Conclusion

Introduction

In this article, we will delve into Azure's infrastructure strategy for workloads related to remote visualization, graphics, gaming, and accelerated compute. We will explore the different kinds of virtual machines available in Azure that support accelerators and their optimization for various workloads. Additionally, we will discuss user profiles in the visualization market and the requirements of different levels of users.

Azure's Infrastructure Strategy

Overview of Virtual Machines

Azure provides a range of virtual machines that are optimized for specific purposes. These virtual machines can support various types of accelerators, including Nvidia GPUs and AMD GPUs. At a high level, there are three main families of virtual machines in Azure: NV, NC, and ND. Each family is powered by different accelerators and is optimized for different workloads.

Different Kinds of Accelerators

The NV line of virtual machines in Azure is specifically optimized for graphics applications such as virtual desktops, remote visualization, and workstations. On the other HAND, the NC and ND lines of virtual machines are focused more on the compute side with accelerators. NC is designed for medium compute workloads, such as rendering and machine learning inference, while ND is optimized for high-end training.

Optimization for Various Workloads

Azure's infrastructure strategy involves providing virtual machines that cater to the needs of different user profiles in the visualization market. The user profiles can be represented by a pyramid, with regular designers or desktop as service users at the bottom, workstation users in the middle, and users with high resource requirements at the top. Azure's virtual machines address these profiles by offering a range of GPU capabilities and system memory capacities.

User Profiles in the Visualization Market

The visualization market encompasses a wide range of users, each with their own specific requirements. Understanding these user profiles is essential for providing the right solutions and virtual machines. At the bottom of the pyramid, we have regular designers or desktop as service users who primarily use integrated graphics chips for their everyday applications. As users transition to the cloud, they expect a similar graphic visualization experience for applications that can benefit from it. Moving up the pyramid, we have workstation users who rely on GPUs for running applications that require enhanced graphics capabilities. At the top of the pyramid, we have users with high resource requirements, such as those needing multiple GPUs or large amounts of memory, for specialized workflows or applications.

Azure's Offerings for Remote Visualization

Azure recognizes the diverse needs of users in the remote visualization segment and provides virtual machines that meet these requirements. With a focus on graphics and GPU capabilities, Azure offers virtual machines that range from those suitable for users who do not require a full GPU to those designed for high-end use cases. By offering a variety of GPU partitioning options along with different memory capacities and front-end networking capabilities, Azure ensures that users are provided with the necessary resources for their specific remote visualization needs.

Moving to the cloud allows users to benefit from features such as GPU visualization for applications that require it. This enables users to seamlessly transition from on-prem laptops to virtual PCs in the cloud, while still enjoying a similar experience. Azure's virtual machines cater to users with varying GPU requirements, offering scalability and flexibility as users increase or decrease their workload demands.

Pros:

• Azure's virtual machines provide a wide range of options to accommodate different user profiles and their specific needs.
• The GPU partitioning feature allows users to scale up or down their GPU resources based on their workload requirements.
• Azure's virtual machines offer enhanced front-end networking capabilities, ensuring high-speed connectivity for graphics-intensive applications.

Cons:

• Some users may find it challenging to determine the optimal virtual machine configuration that meets their specific requirements.

Introduction to Azure's AI Virtual Machines

Azure also offers a range of virtual machines specifically designed for AI workloads. These virtual machines cater to different types of AI workloads, ranging from light compute to high-performance requirements. The AI virtual machines provided by Azure enable users to accelerate their AI workloads and leverage the power of GPUs for improved performance.

NV A10 v5: A New GPU-Enabled Virtual Machine

Azure introduces the NV A10 v5 series as an addition to its existing NVv3 and NVv4 virtual machines. The NV A10 v5 series is based on the Nvidia NPR A10 GPU from the latest NPR line of products. It brings GPU partitioning and introduces GPU operation on Nvidia, addressing the feedback from customers who desired Nvidia GPU support on Azure. The NV A10 v5 series offers increased GPU memory capacities, improved front-end networking throughput, and high-frequency CPU performance. With a focus on user flexibility and enhanced performance, the NV A10 v5 series is set to provide users with a powerful virtual machine option for their AI workloads.

Pros:

• The NV A10 v5 series introduces GPU operation on Nvidia GPUs, expanding the options available to customers.
• Increased GPU memory capacities and improved front-end networking provide enhanced performance for graphics-intensive applications.
• High-frequency CPU performance addresses the need for customers who require a high-performance CPU for their workloads.

Cons:

• Users may require guidance in selecting the appropriate NV A10 v5 configuration based on their specific requirements.

NC and ND Series: Roadmap for AI Virtual Machines

In addition to the NV A10 v5 series, Azure offers the NC and ND series of virtual machines for AI workloads. The NDm A100 v4 virtual machine, powered by the Nvidia A100 GPU, is Azure's high-end distributed AI training platform. It offers superior performance with features such as high-speed networking, support for large-scale models, and top-ranking capabilities in supercomputing.

Azure is constantly working towards innovation and improvement, and plans to release the NCA 100 v4 virtual machine later this year. This mid-range AI training GPU, based on the Nvidia A100 PCIe GPUs, will offer a range of options from one to four per VM. With the latest PCIe 4.0 solution and NVLink bridge connectivity, the NCA 100 v4 series is poised to meet the demands of real-world applied AI workloads, including GPU accelerated analytics, databases, batch inferencing, and more.

Azure's roadmap also includes the upcoming release of the NC8ads v4 virtual machine, which aims to fulfill the future inferencing and light compute platform requirements. Powered by Nvidia A10 GPUs, the NC8ads v4 series supports GPU partitioning and offers a range of GPU options, from half an A10 to full A10 and two A10s. With enhanced performance, support for Azure storage options, and faster networking capabilities, this virtual machine is designed to meet the needs of customers seeking the best cost-performance ratio for their workloads.

Software Platform Features for Azure's AI Virtual Machines

Azure not only provides powerful hardware solutions but also offers additional software platform features to enhance the management and operation of AI workloads. These software offerings aim to make operating, managing, and maximizing the value of AI workloads on Azure hardware easier for users.

Conclusion

Azure's infrastructure strategy focuses on empowering users with virtual machines optimized for their specific workloads, whether it be remote visualization or AI. The variety of virtual machine offerings, GPU capabilities, and software platform features provided by Azure ensure that users can leverage the power of Azure's infrastructure to meet their unique requirements. With ongoing innovation and a roadmap for future enhancements, Azure remains committed to providing cutting-edge solutions for remote visualization and AI workloads.

Highlights:

• Azure offers a range of virtual machines optimized for remote visualization, graphics, gaming, and AI workloads.
• Three families of virtual machines, NV, NC, and ND, cater to different types of workloads and are powered by different accelerators.
• User profiles in the visualization market range from regular laptop users to high-end users with resource-intensive requirements.
• Azure's virtual machines provide GPU partitioning, increased memory capacities, and enhanced front-end networking for optimal performance.
• The NV A10 v5 series introduces GPU operation on Nvidia GPUs and offers improved performance and flexibility for AI workloads.
• The NDm A100 v4 virtual machine is Azure's high-end distributed AI training platform, offering superior performance and scalability.
• The NCA 100 v4 and NC8ads v4 virtual machines are upcoming releases targeted towards mid-range AI training and inferencing/light compute workloads.
• Azure's software platform features enhance the management and operation of AI workloads on its virtual machines.

FAQs:

Q: How do Azure's virtual machines cater to different user profiles in the remote visualization market? A: Azure provides a range of virtual machines with varying GPU capabilities and system memory capacities to meet the needs of different user profiles. From integrated graphics chip support for regular laptop users to high-performance GPUs for resource-intensive workflows, Azure's virtual machines offer scalability and flexibility.

Q: What are the benefits of GPU partitioning in Azure's virtual machines? A: GPU partitioning allows users to scale up or down their GPU resources based on their workload requirements. This provides users with the flexibility to allocate the appropriate GPU resources to their virtual machines, optimizing performance and cost-effectiveness.

Q: What are the key features of the NV A10 v5 series? A: The NV A10 v5 series introduces GPU operation on Nvidia GPUs, providing users with more options for their virtual machines. It offers increased GPU memory capacities, improved front-end networking throughput, and high-frequency CPU performance, enhancing the overall performance for AI workloads.

Q: What are the upcoming releases in Azure's roadmap for AI virtual machines? A: Azure plans to release the NCA 100 v4 virtual machine, which is a mid-range AI training GPU based on the Nvidia A100 PCIe GPUs. Additionally, the NC8ads v4 virtual machine is in development to fulfill the requirements for future inferencing and light compute workloads.

Resources:

• Azure Virtual Machines
• Azure Blog: Accelerated Networking in Azure Virtual Machines