虛擬化GPU與VDI解決方案

結合VDI和vGPU技術,適用於遠距辦公的虛擬桌面解決方案
Introduction
The time is now for Virtual Desktop Infrastructure (VDI) using Virtual GPUs (vGPU) to take hold. Workers and devices are scattered, and employees often need to work remotely. This poses all sorts of challenges in terms of security, productivity, user adaptation, and IT management. Also, there is a shift to a greater need for engineers and designers to use 3D applications and other graphics accelerated applications with large amounts of data. In the past, CPU-only VDI environments were deployed to centralize management; however, these solutions could only reach office workers who did not need graphics acceleration, and power users, designers, and more were left out and had to stick with dedicated machines. Now, VDI has reached the point that all users who need graphics acceleration have a solution by combining VDI with vGPU. GIGABYTE has many servers specifically designed for VDI usage for productivity applications, 3D modeling, HPC applications, AI, and more.
Who needs VDI with Virtual GPUs?
Virtual Workforce
Workforce is shifting, and workers are more commonly needed to do work outside of the office. They also may have to use multiple devices, including tablets or personal phones. VDI allows users to access files remotely, while IT can securely manage the security and privacy concerns.
AI and Deep Learning
Artificial Intelligence and Deep Learning rely on the power of GPUs because most algorithms are parallelizable, which GPUs excel at, and GPUs have incredible memory bandwidth that can handle a lot of data.
Engineering & Sciences
To optimize and test designs in aerodynamics, as well as thermal simulations, these situations require the acceleration that only a GPU can offer. Engineers need access to data or high computing, and with Virtual GPUs the work is possible at a terminal separate from where the data or hardware is based.
Challenges to VDI
Several industries have been quick to adopt VDI, such as Healthcare as they needed greater compliance with regulations, and remote access became a must. However, as companies are pondering the possibility of switching there servers to VDI there are some major challenges ahead.

When we say companies need to adopt new practices we are focusing on the individuals. The switch from a personal device that is always yours to one that is simply logged in can make many users become perturbed. Add this to the fact that not all applications can be virtualized and you have a user that does not want to adopt a new way of using the PC. Convincing and showing employees that in the long term it is worth it must be done. As well, costs upfront are high for VDI architecture. Instead of buying a laptop when a user needs one, all the hardware and setup for VDI must be done before deployment of virtual devices. Last, to deploy virtual devices, IT administrators need to be onboard with the project and require high proficiency in working with servers and may need training. If these challenges can be met companies can reap the benefits of VDI.


Benefits of VDI
suitable to User Friendly, Ease of Use& Lower maintenance requirement
User Experience
Resources can better be tailored to user. As well, PC upgrades and repair takes minutes instead of hours.
suitable to Performance, Efficiency
Productivity & Flexibility
A dedicated, virtualized machine allows users to easily access their work-related resources from anywhere and on any device with internet.
suitable to Security, Data Protection
Ease of Management
One central server location to upgrade hardware or software, as well as monitoring and patching, makes IT admins job easier.
suitable to Quality, Accuracy
Efficiency of Resources
Resources are not underutilized or overprovisioned. Virtual machines have optimal allocation of hardware to fit user.
VDI Architecture Explained
The concept of VDI is to use hardware in a server and allocate it to virtual machines (VM). The image on the right visualizes the allocation of resources. And the image below depicts virtualization architecture that is discussed next.

To start, a server is built with typical hardware (CPU, RAM, storage, GPU, network interconnects, etc.) that has been determined for the end users. After hardware installation, a hypervisor (Microsoft Hyper-V, Citrix XenServer, etc.) is installed to abstract the hardware. This hypervisor creates a virtualization layer upon which the VMs are built. The hypervisor that is between the VMs and the hardware contains a Virtual Machine Manager. Through this technology each VM is added with their own operating system and applications. At this point a server has virtualized all but the GPU.

To create a virtual GPU, software such as NVIDIA Virtual GPU Manager installs in the hypervisor. This software coupled with other NVIDIA software (vCS, vDWS, GRID vPC, or GRID vApps) allows customization of the virtual machine. On this vGPU layer are the VMs as before, but now with NVIDIA drivers and binaries/libraries.
Virtual Machines from a Single Server
Virtualization with and without a Virtual GPU
Determine the GIGABYTE server and NVIDIA Solution
Details below are meant to help one decide on what solution fits best.

・Understand the types of users to select the appropriate NVIDIA Virtual software
・Learn about the different types of NVIDIA GPUs based on specifications and usage
・Choose from the list of GIGABYTE servers that are NVIDIA vGPU certified
Types of VDI Users
Task Worker:
Basic tasks and do not need graphics acceleration.
NVIDIA Virtual Software: GRID vApps
NVIDIA GPU Used: Turing T4

Knowledge Worker:
Entry-level graphics.
NVIDIA Virtual Software: GRID vPC or Quadro vDWS
NVIDIA GPU Used: Turing T4

Power User:
Mid-level graphics to high.
NVIDIA Virtual Software: Quadro vDWS or vCS
NVIDIA GPU Used: Quadro RTX6000 or Turing T4

Designer, Engineer, AI Scientist:
High-end computing.
NVIDIA Virtual Software: vCS
NVIDIA GPU Used: Ampere A100, Quadro RTX8000, or Turing T4
Programs Used by Worker Type
NVIDIA Accelerators for Virtualized Environment
A100 PCIe RTX A6000 A40 Quadro RTX 8000 Quadro RTX 6000 T4
Architecture Ampere Ampere Ampere Turing Turing Turing
CUDA cores 6,912 10,752 10,752 4,608 4,608 2,560
Single-Precision 19.5 TFLOPS TBD TBD 16.3 TFLOPS 16.3 TFLOPS 8.1 TFLOPS
GPU Memory 40 GB HBM2 48 GB GDDR6 48 GB GDDR6 48 GB GDDR6 24 GB GDDR6 16 GB GDDR6
Memory Bandwidth 1.6 TB/s 768 GB/s 696 GB/s 672 GB/s 624 GB/s 320 GB/s
Interface PCIe Gen 4 PCIe Gen 4 PCIe Gen 4 PCIe Gen 3 PCIe Gen 3 PCIe Gen 3
Max Power 250W 300W 300W 295W 295W 70W
Form Factor dual-slot dual-slot dual-slot dual-slot dual-slot single-slot
Usage Ultra-high-end rendering, 3D design, AI and data science High-end rendering, 3D design, AI, and compute workloads Mid-range to high-end 3D design and creative workflows High-end rendering, 3D design, and creative workflows Mid-range to high-end rendering, 3D design and engineering, AI and data science Entry-level to high-end 3D design and engineering, AI and data science


GIGABYTE Servers (NVIDIA vGPU Certified)
NVIDIA Models A100 PCIe Quadro RTX 8000 Quadro RTX 6000 T4
1U G-series G191-H44 G191-H44 G191-H44 G191-H44
1U OCP-series - - - T181-G23, T181-G23-G24, T181-Z70
2U G-series G291-280, G291-281, G292-Z20, G291-Z40 G291-280, G291-281, G242-Z10, G292-Z42 G291-280, G291-281, G242-Z10, G292-Z42 G291-280, G291-281, G221-Z30, G242-Z10, G291-Z20, G292-Z42
2U R-series R281-3C2, R281-G30, R282-Z93 R282-Z93 - R281-G30, R281-3C2, R282-Z93
2U H-series - - - H231-G20
4U G-series G481-HA0, G482-Z50, G492-Z50, G492-Z51 G481-H80, G481HA0 G481-H80, G481-HA0, G481-HA1, G482-Z50, G481-51 G481-H80, G481-HA0, G481-HA1, G482-Z51

Products
1/2
2U GPU Server for Scale-out
G242-Z11 (rev. 100)
Ideal for scale-out deployment in virtualization for GPU-centric workloads. High core count AMD EYPC™ processor and up to 4 GPUs with direct PCIe Gen4 x 16 connection to CPU. Also, 4 x 3.5" SATA and 2 x 2.5" U.2 (Gen 4)
2/2
1U GPU Server for Small Form Factor
G191-H44 (rev. 100/200)
Ideal to scale up for 5G network infrastructure or deployment in a small space. Dual Intel Xeon Scalable processors and up to 4 full-length full-height GPUs.
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