Eight Key Considerations for AI Infrastructure


Discover eight key factors to guide your AI infrastructure decisions, ensuring a scalable, cost-effective and compliant solution.

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These eight key factors help you design a future-proof and efficient AI infrastructure

Selecting the optimal AI architecture is crucial for seamless performance, scalability, and efficiency.

AI Architecture Options:

  • NVIDIA DGX – Purpose-built, turnkey reliability for high-performance AI workloads. Ideal for enterprises needing scalability, reliability, and easy deployment.
  • NVIDIA HGX – Customizable flexibility with OEM solutions tailored to unique needs. Best suited for organizations requiring specific hardware configurations.
  • Workstations – Scalable desktop AI solutions for developers, researchers, and small-scale AI projects needing local compute power.

Key factors to consider:
✅ Type of AI workload – GenAI, Vision, Speech, Training, Inference?
✅ In-House Expertise – What competencies are available?
✅ Performance Needs – Requirements around processing speed and latency.

The right GPU is crucial for AI performance, software compatibility, and long-term support. The common choices today are:

  • NVIDIA – Industry-leading AI performance with a well-optimized software ecosystem (CUDA, TensorRT, Triton) and extensive developer support.
  • AMD – Competitive performance with an open software ecosystem (ROCm), offering an alternative for specific workloads.

GPU selection depends on various factors, including:
✅ Software compatibility – Does your AI stack rely on CUDA or other frameworks?
✅ Performance requirements – Compute power, memory bandwidth, and workload scalability.
✅ Ecosystem & support – Availability of AI tools, libraries, and long-term vendor backing.

Selecting the right networking and storage is essential for AI performance, scalability, and efficiency.

Networking:

  • InfiniBand – Ultra-low latency, high-bandwidth for AI clusters.
  • Ethernet (100/400GbE) – Scalable and cost-effective for AI workloads.
  • RDMA & GPUDirect – Faster data transfers, reducing bottlenecks.

Storage:

  • NVMe SSDs – High-speed, low-latency for AI training & inference.
  • Parallel File Systems – Scalable storage for large AI datasets.
  • Object Storage – Cost-effective for long-term AI data retention.

Key factors to consider:
✅ Speed & Latency – Can storage & networking handle AI demands?
✅ Scalability – Supports growth in AI workloads & datasets.
✅ Efficiency – Optimized data access for uninterrupted AI training.

The choice between cloud, owned, or hybrid AI infrastructure depends on flexibility, control, sustainability, compliance, cost efficiency, and scalability.

Cloud Solutions:

  • Flexible & Scalable – Quick deployment with a pay-as-you-go model.
  • Managed Services – Reduces operational overhead but comes with vendor lock-in.

Owned AI Stack:

  • Full Control – Optimized performance, compliance, and control.
  • Cost Efficiency – Scaling up makes ownership more financially viable.
  • Deployment Options – Can be hosted on-site or with a colocation partner.
  • Building Expertise – Developing in-house skills can create a competitive advantage.

Hybrid Approach:

  • Best of Both Worlds – Combines cloud flexibility with the control of an owned stack.
  • Workload Optimization – Allocate workloads based on performance, cost, and compliance needs.

Key factors to consider:
✅ Workload predictability – Steady vs. variable AI demand?
✅ Data control – Compliance and sovereignty requirements?
✅ Long-term costs – Cloud expenses vs. infrastructure investment?
✅ Scalability – Does growth justify an owned AI infrastructure?
✅ Latency & Performance – Hosting choices impact response times.
✅ Data Gravity – Is processing data where it is generated more efficient?
✅ Strategic Advantage – Does owning AI infrastructure create unique capabilities?

The location of your AI infrastructure affects scalability, security, and operational efficiency.

On-Site:

  • Full Control – Direct oversight of security, data governance, and infrastructure.
  • Resource Availability – Requires sufficient in-house capacity (space, power, cooling, expertise).

Colocation:

  • Scalable & AI-Ready – Leverage external facilities optimized for AI workloads.
  • Cost & Energy Efficiency – Benefit from shared infrastructure and advanced cooling solutions.

Modular Data Center:

  • Turnkey AI Solution – Pre-built, scalable, and fully secure infrastructure.
  • Flexibility – Can be deployed at strategic locations close to data sources.

Key factors to consider:
✅ Scalability – Can the location support future AI growth?
✅ Security & Compliance – Do data regulations require specific hosting locations?
✅ Energy & Cooling – Does the infrastructure support AI’s high power demands?
✅ Latency & Proximity – Does the AI workload benefit from being close to data sources?

The chosen management model impacts operational efficiency, costs, and your team's strategic focus.

Self-Managed:

  • Full Control – In-house teams handle operations, optimizations, and security.
  • Expertise Development – Builds internal knowledge for long-term AI success.

Partner-Managed:

  • Operational Simplicity – Offload management to AI infrastructure experts.
  • Optimized Performance – Ensure seamless operation with proactive support.

Key factors to consider:
✅ In-House Expertise – Does your team have the skills to manage AI infrastructure?
✅ Resource Allocation – Do you want to focus on infrastructure or AI development?
✅ Reliability & Support – Do you need 24/7 monitoring and proactive maintenance?

Financial considerations are crucial for AI infrastructure planning, affecting cost efficiency, scalability, and long-term strategy.

OPEX:

  • Flexible Financing – Lease or subscription-based models for an owned AI stack.
  • Lower Initial Investment – Spread costs over time.

CAPEX:

  • Strategic Investment – Upfront purchase of AI infrastructure for long-term cost savings.
  • Full Cost Control – No recurring payments, reducing financial dependencies.

Life Cycle Management:

  • Future-Proofing – Ensure timely upgrades to maintain peak performance.
  • Cost Predictability – Structured refresh cycles prevent unexpected capital expenses.

Key factors to consider:
✅ Financial Strategy – Spread costs (OPEX) or invest upfront (CAPEX)?
✅ Workload Stability – Is predictable usage worth a long-term investment?
✅ Long-Term Savings – Does financing align with cost efficiency goals?
✅ Technology Refresh – How will AI infrastructure be upgraded over time?

Empowering your team with the right tools, knowledge, and support is essential for AI success.

In-House Competencies:

  • Skill Development – Train teams to build, manage, and optimize AI infrastructure.
  • Cross-functional collaboration – Foster AI expertise across departments for long-term self-sufficiency.

External Competencies:

  • Expert Guidance – Leverage AI specialists for best practices and advanced insights.
  • Operational Support – Access external expertise to accelerate implementation and optimize performance.

Ongoing Support:

  • Continuous Learning – Keep teams up to date with evolving AI technologies.
  • Proactive Maintenance – Ensure reliability with long-term infrastructure support.

Key factors to consider:
✅ Internal vs. External Balance – What expertise should be developed in-house?
✅ Training Needs – Does your team require AI infrastructure or model development skills?
✅ Long-Term AI Strategy – How will ongoing education and support be structured?
✅ Maintenance – Who ensures infrastructure remains efficient and up to date?

Let us help you design your AI infrastructure

Each factor determines how well your infrastructure supports control, sovereignty, scalability and sustainability.

Your AI, Your Way