Accelerated Compute Readiness Checklist
A guide to help you assess your organization’s readiness to support AI with the right accelerated compute strategy across modern infrastructure and hybrid cloud environments.
Is Your Organization Prepared to Scale AI Without Adding Unnecessary Cost, Complexity and Technical Debt?
AI is pushing infrastructure decisions into a new phase, and teams are hearing two competing directives: Move fast with cloud-first adoption, but also evaluate repatriation as costs, latency and governance pressures grow. It’s not surprising that 73% of organizations now operate hybrid cloud environments, and both cloud-based workloads and data repatriated from the cloud have increased year over year.1
That tension is exactly why accelerated compute matters. It is not just a matter of buying more GPUs. It’s an architecture and operating model decision — matching the right compute to the right workload, placing it where it performs best and managing it without creating platform sprawl, duplicated tooling or long-term technical debt.
Use this checklist to help evaluate whether you have the strategy, placement model and operational discipline to make accelerated compute a scalable capability.
Five Steps to Accelerated Compute Readiness
Strategy: Define the Outcome Before You Define the Platform
Start with the AI goal, not the hardware. Clarify what the business needs the workload to do, what performance matters most and what success looks like in production.
- Have you identified the AI use cases that matter most to the business and ranked them by business value, urgency and feasibility?
- Have you defined workload requirements such as latency tolerance, throughput, data access, resilience and governance needs before selecting infrastructure?
- Do you know which workloads are still experimental and which are expected to scale into long-term operations?
Placement: Put Workloads Where They Perform Best
Workload placement is one of the biggest drivers of AI performance and cost. In many environments, cloud is the right fit for burst capacity, especially for training and experimentation, while on-premises can be the better fit for data proximity, responsiveness and control, especially for inference and operations at scale.
- Do you have clear criteria for deciding what should run in public cloud, what should run on-premises and what should move between the two over time?
- Have you evaluated whether data movement, latency or governance requirements make certain workloads better suited to run closer to where data lives?
- Can you distinguish between cloud-first experimentation and mature workloads that may justify repatriation or a different long-term placement strategy?
- Do you have a repeatable process for reviewing placement decisions as usage, cost and performance requirements change?
Right-Sizing: Match Acceleration to the Workload
Not every AI workload needs the same level of acceleration. The goal is to avoid over-provisioning, contain cost and deliver the right performance for training, tuning and inference.
- Have you mapped which workloads truly require GPU acceleration and which can run effectively on CPUs or smaller accelerated footprints?
- Do you understand the storage, network and memory demands needed to support accelerated workloads without creating bottlenecks?
- Are you sizing infrastructure for actual workload behavior instead of buying for worst-case assumptions?
- Can you scale capacity in modular increments so you do not overbuild too early or stall later?
Facility Readiness: Plan for the Physical Demands of Accelerated Compute
Accelerated compute can expose physical constraints earlier than many teams expect. Modern accelerated servers may introduce higher power draw, thermal output and rack density than traditional infrastructure, making facility readiness a gating factor for AI at scale. Planning for these requirements early can help prevent delays after strategy, budget and architecture decisions are already in motion.
- Have you assessed whether existing power, cooling and space capacity can support higher-density accelerated compute deployments?
- Do infrastructure, facilities and operations teams have a shared plan for evaluating site readiness before new AI workloads move into production?
- Have you identified whether rack density, thermal limits or support requirements could slow expansion in current environments?
- Can you scale in phases so facility upgrades and compute investments stay aligned as demand grows?
- Do you have a clear process for reassessing facility needs as accelerated compute usage grows over time?
Operations: Reduce Sprawl and Maintain Control
Accelerated compute can increase operational burden if every team uses different tools, policies and monitoring approaches. Centralized management helps reduce platform sprawl and keep governance consistent across environments.
- Do you have a consistent way to apply policy, monitor usage and manage AI resources across cloud and Azure‑consistent on‑premises environments?
- Are infrastructure, security, data and finance teams aligned on roles, ownership and decision-making for accelerated compute?
- Can you measure utilization, cost and business value without relying on disconnected tools or manual reporting?
Source: 1 Flexera, “2026 State of the Cloud Report,” March 2026
Why CDW
CDW helps organizations move from AI ambition to an accelerated compute strategy that is practical, scalable and aligned to real workload needs.
- Assessment and requirements mapping: We help evaluate AI use cases, define workload requirements and identify the performance, latency, governance and data needs that should shape infrastructure decisions.
- Workload placement and design guidance: CDW helps determine what belongs in cloud for burst capacity, what belongs on-premises for control and responsiveness, and how to design a modern infrastructure approach that supports both.
- Operationalization and scale planning: We help build the management model for accelerated compute — from visibility and governance to right-sizing, modular growth and planning for real-world constraints such as power and cooling.
Request an Accelerated Compute Readiness Assessment From CDW
Our experts will help you assess AI workloads, evaluate placement options and build a modern infrastructure strategy for accelerated compute.