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5 key priorities for IT leaders to help them scale their AI initiatives
From building an integrated tech stack to working with AI models and enabling IT teams with AI skills, the following are the top priorities for IT leaders.
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Build a holistic infrastructure strategy
A holistic infrastructure strategy aims to build a vertically integrated technology stack where all the necessary IT resources are seamlessly connected. This allows IT teams to build a robust platform for launching AI initiatives.
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Focus on the right deployment model
The right choice for your project can be determined by understanding your cost burdens, computational needs, security restrictions and long-term vision.
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Consider the ROI of accelerated AI infrastructure
AI projects thrive on modern innovations like parallel processing chipsets that outperform traditional CPUs. As your AI initiative matures, it’s worth considering acquiring accelerated chipsets that offer significantly higher processing power.
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Cultivate AI proficiency within the organization
Managing complex AI initiatives that leverage a blend of cloud and PaaS offerings often requires trained professionals with years of experience. Focus on training teams on core AI skills and growing an AI talent pool with dedicated hiring programs.
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Budget for long-term AI goals
The focus should be on balancing TCO with the expected ROI of the infrastructure the organization wants to invest in. That’s why it’s key to understand the scale and shape of the AI project.
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How NVIDIA and CDW can help IT leaders evolve their AI infrastructure
By leveraging NVIDIA’s advanced AI computing solutions and CDW’s expertise in designing and managing AI technologies, organizations can build a sustainable AI infrastructure.
How IT Leaders Can Scale & Sustain Their AI Infrastructure with NVIDIA® and CDW
Here are 5 key priorities that can help IT decision-makers build an AI infrastructure they can easily scale and sustain in the long run. We also highlight AI solutions from our partners at NVIDIA that can be instrumental in overcoming AI bottlenecks.
As Canadian organizations aim for AI maturity, building a resilient AI infrastructure that can power production-ready applications and deliver long-term growth has become a key goal.
For IT leaders, this translates to several key decisions at the ground level. From choosing the right vertically integrated tech stack to achieving cloud readiness, they need technologies that can support the core demands of an AI project.
IT leaders can get a significant head start by focusing on a holistic infrastructure strategy that balances total cost of ownership (TCO) with performance needs early on.
In this blog, we discuss five key priorities that can help IT decision-makers build an AI infrastructure they can easily scale and sustain in the long run. We also highlight AI solutions from our partners at NVIDIA that can be instrumental in overcoming AI bottlenecks.
5 key priorities for IT leaders to help scale their AI initiatives
From building an integrated tech stack to working with AI models and enabling IT teams with AI skills, the following are the top priorities for IT leaders.
1. Build a holistic infrastructure strategy
The infrastructure required to deploy an AI project often spans multiple IT resources. This includes high-end servers, digital data storage, cloud services and software components.
An organization may choose to own the complete tech stack, from servers to software, or consume different components as disparate services.
A holistic infrastructure strategy aims to build a vertically integrated technology stack where all the necessary IT resources are seamlessly connected. This allows IT teams to build a robust platform for launching AI initiatives without worrying about performance or connectivity challenges.
Whether you consume AI services as APIs or train your own models, the infrastructure supporting your AI project should operate in tandem with other components.
This approach brings the following benefits to your AI project:
- Ready for scaling – Makes it easier to scale the project to more users in the future without rearchitecting the underlying infrastructure
- Efficient model deployment – Deploy and experiment with new models to achieve peak performance for the intended use cases
- Simpler resource allocation – Monitor and configure IT resources centrally to better control costs and forecast TCO
- Control over compliance and security – Achieve better security with an infrastructure that has fewer loopholes and vulnerabilities
2. Focus on the right deployment model
The deployment model refers to how the underlying infrastructure is configured and plays a pivotal role in enabling an AI project.
Most organizations either purchase the necessary hardware to run AI projects locally or use cloud services to manage compute-intensive workloads. The right choice for your project can be determined by understanding your cost burdens, computational needs, security restrictions and long-term vision.
The three major deployment models are described below.
Deployment Model |
Operating expenditure |
Capital expenditure |
Long-term scalability |
Security |
Compute power |
On-premises architecture |
Low |
High |
Difficult as adding new hardware may increase CAPEX |
High |
Limited |
Cloud-first approach |
High |
Low |
Easy as new resources can be quickly configured |
Moderate |
Highly available |
Hybrid multicloud approach |
Moderate |
Low |
Easy but requires a strong management function |
High but requires additional focus on vendor compliance |
Highly available for select workloads |
Scenarios for deciding the right deployment model
- High security and more infrastructure control – In scenarios where organizations need to control data security and server-level functions, an on-premises approach may prove ideal. It comes with higher costs but allows stricter compliance and reduced AI risks.
- High scalability and low CAPEX – A cloud-first approach is a better fit when projects need quick scaling and anticipate eventual growth in the user base. It allows startups and small businesses to access key services without making high CAPEX investments.
- Specific workload requirements – If an organization wants the flexibility of cloud alongside improved data privacy, a hybrid approach may fit the bill. IT teams can host select workloads, such as data storage or elastic compute, in the cloud while keeping core infrastructure on-premises.
3. Consider the ROI of accelerated AI infrastructure
AI projects thrive on modern innovations like parallel processing chipsets that outperform traditional CPUs for AI tasks. As your AI initiative matures, it’s worth considering the acquisition of accelerated chipsets that offer significantly higher processing power.
A GPU-based architecture can accelerate tasks related to model training, natural language processing and image recognition, all of which are core AI use cases.
Our partners at NVIDIA offer specialized AI infrastructure services that can help organizations meet their computational needs. Their NVIDIA NIM™ Agent Blueprints are inference microservices that provide a complete inference stack for production deployment of open-source community models, custom models and NVIDIA AI Foundation models.
The solution can lower the TCO for projects with high-end AI computational requirements by shifting to a consumable API architecture.
4. Cultivate AI proficiency within the organization
With the unprecedented pace at which AI technologies are growing, many organizations are facing shortages in the skills needed to lead AI projects.
Managing complex AI initiatives that leverage a blend of cloud and PaaS offerings often requires trained professionals with years of experience. IT directors need to focus on training their teams on core AI skills and growing their AI talent pool with dedicated hiring programs.
Organizations that need industry experts on larger AI projects can partner with CDW Canada for advice on key technical aspects. Our AI experts can help organizations source, build and manage the right AI infrastructure from an early stage.
5. Budget for long-term AI goals
When spearheading an AI project within your organization, it’s critical to develop a long-term vision. As organizations purchase the necessary infrastructure, they often compare the TCO with the total return on investment (ROI) they will receive over the span of the project.
For instance, choosing an API-driven infrastructure for core AI workloads may offer a lower TCO in the long run in comparison to purchasing a massive inference server. If the project vision is to grow AI capabilities and build a modular infrastructure, APIs can help IT teams overcome complexity in the long run.
At the same time, if the project requires training and improving AI models over years, a local high-capacity server would offer better ROI.
The focus should be on balancing TCO with the expected ROI of the infrastructure the organization wants to invest in. That’s why it’s key to understand the scale and shape of the AI project.
How NVIDIA and CDW can help IT leaders evolve their AI infrastructure
By leveraging NVIDIA’s advanced AI computing solutions and CDW’s expertise in designing and managing AI technologies, organizations can build a sustainable AI infrastructure.
Our partners at NVIDIA empower organizations to build a scalable and sustainable AI infrastructure by simplifying AI deployment, ensuring enterprise-grade reliability and offering the flexibility to run AI workloads anywhere.
Key NVIDIA AI infrastructure offerings
- NVIDIA NIM – An inference microservices architecture that accelerates time to deployment by providing production-ready AI models in software containers, accessible via industry-standard APIs.
- NVIDIA NeMo™ – A platform for developing customized AI applications using techniques like model training, retrieval-augmented generation (RAG), etc.
- NIM Agent Blueprints – Pre-trained AI workflows for specific use cases that can be used to quickly configure NVIDIA services as per organizational data and needs.
These offerings are backed by support for enterprise-level applications from NVIDIA that ensure organizations face minimum complexity. The NVIDIA AI Enterprise ecosystem is built in a way that promotes holistic management and compatibility with multiple deployment models.
CDW AI experts work together with NVIDIA to bring the power of these solutions to your organization. We participate in the entire AI journey to help you formalize use cases, select the right solution and configure it as per your data architecture.
Our capabilities cover a diverse range of solutions from NVIDIA including Pro Visualization workflows, AI compute using the NVIDIA DGX™ platform and virtualization use cases with NVIDIA vGPU software.