The key to starting in AI may be right under your nose. It’s all about seeing the potential in the tools and resources that you already have. Adopt a crawl, walk, run approach by beginning your AI journey with small projects to learn from early success before scaling up to production.
According to a Deloitte survey, 83% of respondents said their companies have already achieved either moderate (53%) or substantial (30%) economic benefits from their AI projects. To succeed, it’s crucial to choose a preproduction environment that is both flexible and secure to keep pace with the rapidly evolving technology.
Virtual desktop infrastructure (VDI) provides IT teams with the ability to deliver desktop and app virtualization for remote workforces, enabling them to carry out daily business operations. While VDI has been around for over a decade, advancements in GPU technology have evolved its capabilities to support cutting-edge applications like real-time rendering and virtual reality.
Breakthroughs in GPU architecture and virtual GPU software have made VDI an ideal entry point for corporations starting AI projects, offering a compelling mix of benefits that streamline both development and deployment.
Enabling AI with VDI
There are three essential elements to build an AI virtual workstation from your data center:
- Tensor Core GPUs: Data center GPUs such as the NVIDIA L40S, NVIDIA L4, and NVIDIA A10, equipped with Tensor Cores, can significantly enhance generative AI performance. The latest generation of Tensor Cores deliver 4x speedups in training trillion-parameter generative AI models to a 30x increase in inference performance.
- NVIDIA vGPU software: NVIDIA RTX Virtual Workstation (vWS), part of the NVIDIA vGPU software editions, uses NVIDIA RTX technology to boost computing capacity. By dividing GPU resources across multiple VMs or allocating multiple GPUs to a single VM, this software improves IT manageability and centralizes applications and data.
- AI vWS Toolkits: Tailored to various use cases, AI vWS Toolkits offer reference information including quickstart guide, walkthrough demos, deployment guides, and sizing guides for both IT and AI developers. Featuring NVIDIA developer tools such as NVIDIA AI Workbench and NVIDIA Developer Program with access to NVIDIA NIM for Developer and more, these toolkits help you quickly develop, customize, and fine-tune AI projects for integration into business applications.
Benefits of AI virtual workstations
Using your existing VDI to build AI virtual workstations brings several benefits to an organization’s AI efforts:
- Cost-effective implementation: Using NVIDIA GPUs and NVIDIA RTX Virtual Workstation immediately enables AI workloads on a virtualized infrastructure that typically supports productivity and graphics applications. IT can quickly spin up virtual machines for data scientists and developers within the same platform, eliminating the need for new infrastructure and validation.
- Flexibility and manageability: Unlike traditional enterprise applications, AI applications rapidly evolve as model sizes increase. NVIDIA vGPU software enables IT to partition a GPU into multiple vGPUs and adjust VM frame buffers instantly. The capability of supporting different user types (power user and knowledge worker) or different frame buffers (0.5–48 GB) on a single GPU maximizes GPU utilization. IT can centralize infrastructure management and extend the reach of VDI to the entire workforce.
- Data and security: VDI improves an organization’s overall security and safeguards against disasters while ensuring that designs, data, and intellectual property are centrally stored in the data center. Integrating AI with VDI avoids AI silos and ensures adherence to IT standards for visibility, security, and governance, bringing AI closer to the data.
- Smooth transition to production: After preproduction projects or proofs of concept validate AI effectiveness, transitioning to production requires enterprise-grade stability, security, and support. Production-ready solutions, like NVIDIA AI Enterprise, facilitate this seamless advancement.
Proven performance to accelerate AI
NVIDIA RTX Virtual Workstation (vWS) enables NVIDIA RTX technology and GPU acceleration from the data center to excel AI development, making it an ideal entry point for organizations embarking on their AI journey.
Figure 2 shows that the latest NVIDIA Ada Lovelace Architecture GPU, L40S, delivers more than 2x acceleration over the NVIDIA Turing architecture GPU, when testing a Llama-3-8B model to build a RAG application on NVIDIA vWS.
Based on the same test, Figure 3 shows the vGPU scalability performance by measuring the effect of GPU sharing on overall throughputs across different GPU partitioning options, ranging from a full GPU to half and one-third of an L40S GPU.
Virtualized GPUs offer significantly higher total throughput compared to passthrough because of their ability to efficiently share resources among multiple virtual machines.
By using NVIDIA vGPU technologies, your organization can dynamically allocate GPU resources to various workloads, maximizing utilization and minimizing idle time. This enhances AI development that requires intensive accelerated processing and also enables better scalability as demand fluctuates.
Get started with your AI development
By using existing resources, GPU-powered VDI offers a blend of agility and flexibility that is crucial for corporations adopting AI on a manageable scale. Its minimal cost to upgrade ensures that your organization can invest in AI initiatives without excessive financial strain while maintaining operational efficiency and security.
For more information, see the following resources:
- Jumpstart AI Development with Virtual Workstations
- NVIDIA vGPU software evaluation license (free 90-day license)
- AI vWS Toolkits
NVIDIA RTX Virtual Workstations are also available in major CSP marketplaces for immediate access.
