June 12, 2026
Accelerated Compute: Infrastructure That Supports AI
The right approach to infrastructure can deliver performance, scalability and cost control in an increasingly artificial intelligence-driven world.
Artificial intelligence adds complexity to infrastructure decisions as organizations strive to match varied workload needs with available compute resources. Accelerated compute is essential for certain workloads, but its value depends on properly aligning the compute with the workload. Organizations will achieve the best results in performance, cost and scalability when they shift from a hardware-first mindset to a workload-first strategy that takes business outcomes into account.
AI training, inference, analytics and edge processing each place different demands on infrastructure. The best environments are designed holistically and with these differences in mind, together with considerations about latency, compliance and data accessibility. Success also requires continuous optimization because workload needs, data placement and scaling requirements change over time. CDW can help organizations make these decisions with a focus on business outcomes, aligning infrastructure decisions to performance, efficiency and long-term value.
Align accelerated compute with artificial intelligence workloads and business outcomes.
Artificial intelligence adds complexity to infrastructure decisions as organizations strive to match varied workload needs with available compute resources. Accelerated compute is essential for certain workloads, but its value depends on properly aligning the compute with the workload. Organizations will achieve the best results in performance, cost and scalability when they shift from a hardware-first mindset to a workload-first strategy that takes business outcomes into account.
AI training, inference, analytics and edge processing each place different demands on infrastructure. The best environments are designed holistically and with these differences in mind, together with considerations about latency, compliance and data accessibility. Success also requires continuous optimization because workload needs, data placement and scaling requirements change over time. CDW can help organizations make these decisions with a focus on business outcomes, aligning infrastructure decisions to performance, efficiency and long-term value.
Align accelerated compute with artificial intelligence workloads and business outcomes.
Artificial intelligence adds significant complexity to infrastructure decisions and should be considered when organizations are approaching their data center refresh. Traditional refreshes were not designed with AI in mind, and if customers use an antiquated approach for modernized business outcomes, it can lead to overinvestment and misaligned architecture. Today, organizations must view their workload environments holistically, recognizing that while AI increases compute demand, not all workloads require the same access to available resources. Accelerated compute lets organizations leverage specialized resources — GPUs, AI-optimized CPUs and other accelerators — to optimize performance, efficiency and outcomes.
Modern CPUs can support certain AI workloads, but others require accelerated compute for scale and performance. While the cloud can provide flexible capacity for high-intensity model training and periodic bursts, AI inferences tend to be lower-intensity and latency-sensitive — often better suited to on-premises infrastructure. Analytics may be moderately to highly compute-intensive, while edge processing prioritizes reduced latency and high responsiveness, typically without requiring highly performant compute.
However, many organizations lack a clear and holistic understanding of their workload variations. As a result, they treat all workloads the same, which leads to overprovisioning, abandoned resources and inevitably higher costs. Misaligned infrastructure is likely to create performance bottlenecks and slower response times, poor user and customer experiences, productivity loss, and missed insights. In addition to the day-to-day impact of these outcomes, they ultimately represent lost revenue opportunities and a negative effect on the bottom line.
To manage these risks, organizations should design infrastructure that supports their current plans for AI workloads while also being adaptable for growth and expansion in the future. This approach recognizes that while accelerated compute is not new, its role in enterprise AI is expanding and changing as AI workloads become more complex. By leveraging accelerated compute strategically, organizations can achieve the performance, efficiency and scalability they need in an increasingly AI-driven world.
41%
The percentage of financial institutions that use hybrid architectures to optimize costs by running different workloads in different environments
Source: resources.nvidia.com, “State of AI in Financial Services: 2026 Trends,” January 2026
CDW can help you use accelerated compute to improve your artificial intelligence outcomes.
Artificial intelligence adds significant complexity to infrastructure decisions and should be considered when organizations are approaching their data center refresh. Traditional refreshes were not designed with AI in mind, and if customers use an antiquated approach for modernized business outcomes, it can lead to overinvestment and misaligned architecture. Today, organizations must view their workload environments holistically, recognizing that while AI increases compute demand, not all workloads require the same access to available resources. Accelerated compute lets organizations leverage specialized resources — GPUs, AI-optimized CPUs and other accelerators — to optimize performance, efficiency and outcomes.
Modern CPUs can support certain AI workloads, but others require accelerated compute for scale and performance. While the cloud can provide flexible capacity for high-intensity model training and periodic bursts, AI inferences tend to be lower-intensity and latency-sensitive — often better suited to on-premises infrastructure. Analytics may be moderately to highly compute-intensive, while edge processing prioritizes reduced latency and high responsiveness, typically without requiring highly performant compute.
However, many organizations lack a clear and holistic understanding of their workload variations. As a result, they treat all workloads the same, which leads to overprovisioning, abandoned resources and inevitably higher costs. Misaligned infrastructure is likely to create performance bottlenecks and slower response times, poor user and customer experiences, productivity loss, and missed insights. In addition to the day-to-day impact of these outcomes, they ultimately represent lost revenue opportunities and a negative effect on the bottom line.
To manage these risks, organizations should design infrastructure that supports their current plans for AI workloads while also being adaptable for growth and expansion in the future. This approach recognizes that while accelerated compute is not new, its role in enterprise AI is expanding and changing as AI workloads become more complex. By leveraging accelerated compute strategically, organizations can achieve the performance, efficiency and scalability they need in an increasingly AI-driven world.
CDW can help you use accelerated compute to improve your artificial intelligence outcomes.
AI Infrastructure by the Numbers
73%
The percentage of organizations that train or fine-tune AI models using a public cloud provider, versus 50% in their own data center
Source: amd.com, “AI Infrastructure for Business Impact: Enabling Agentic Intelligence with Scalable Compute,” October 2025
61%
The percentage of organizations that view specialized infrastructure (servers, storage and networking) as an important cost consideration before taking an AI model into production
Source: amd.com, “AI Infrastructure for Business Impact: Enabling Agentic Intelligence with Scalable Compute,” October 2025
35%
The percentage of IT decision-makers who said their IT infrastructure is “somewhat ready,” “slightly ready” or “not at all” ready to handle AI workloads
Source: CDW.com, “CDW Artificial Intelligence Research Report,” April 2025
AI Infrastructure by the Numbers
73%
The percentage of organizations that train or fine-tune AI models using a public cloud provider, versus 50% in their own data center
Source: amd.com, “AI Infrastructure for Business Impact: Enabling Agentic Intelligence with Scalable Compute,” October 2025
61%
The percentage of organizations that view specialized infrastructure (servers, storage and networking) as an important cost consideration before taking an AI model into production
Source: amd.com, “AI Infrastructure for Business Impact: Enabling Agentic Intelligence with Scalable Compute,” October 2025
35%
The percentage of IT decision-makers who said their IT infrastructure is “somewhat ready,” “slightly ready” or “not at all” ready to handle AI workloads
Source: CDW.com, “CDW Artificial Intelligence Research Report,” April 2025
- WORKLOAD-FIRST STRATEGY
- OPTIMIZE AI WORKLOAD PLACEMENT
- ENABLING AI OUTCOMES
When organizations understand their workloads across their full hybrid data center landscape, they can optimize infrastructure throughout the organization rather than analyzing their various business units’ workloads in silos. A holistic assessment allows for economies of scale and stronger cost controls by carefully matching AI workloads to the right compute resources.
START WITH OUTCOMES: Designing effective AI infrastructure starts by identifying the business outcomes that map to specific workloads and then to compute resources. Without these analyses, organizations risk investing in unneeded infrastructure or architecture that fails to deliver the required performance and the desired ROI. Drawing a clear line from outcomes to workloads and then compute ensures that workload requirements, data accessibility, latency and performance are aligned.
A COMPREHENSIVE PICTURE: As organizations’ AI adoption evolves and matures, so will the supporting workloads, and with that increased diversity of those workloads. This added complexity required a thorough evaluation of cost, ease of use, IT preferences, and skill sets within the business. Organizations are more likely to address these considerations when they design infrastructure for the entire portfolio rather than siloed use cases. Holistic planning improves performance, utilization, efficiency and economies of scale.
DATA CONSIDERATIONS: Data management is crucial for AI and a challenge for many organizations. Research indicates that 40% of financial institutions consider data-related issues their top AI challenge, and 84% of organizations across industries say their data is not fully optimized for AI and large language model ingestion. Data governance, integration and operations play an integral role in infrastructure modernization decisions. A comprehensive data management strategy will allow for businesses to ensure that their data has be properly prepared for the outcomes their business success demands, and by extension provide an informed approach when it comes to designing a productive and well-optimized hybrid data center environment.
CAPACITY PLANNING: Capacity planning requires a clear understanding of baseline workloads and how they will shift during periods of high intensity. While the cloud is the primary lever for scaling resources, it isn’t always an option; organizations may need to plan for future growth by prioritizing compatible, on-premises hardware.
Data center teams often benefit from workload scheduling and orchestration tools that allocate and coordinate workloads efficiently. Teams can also facilitate capacity planning — and ensure that mission-critical workloads are prioritized — by following defined policies for where to run production versus experimentation workloads.
Cloud rebalancing helps organizations optimize capacity planning for AI workloads by dynamically shifting compute resources across cloud environments based on demand, cost and performance requirements. This enables IT teams to avoid overprovisioning, reduce infrastructure costs, and ensure critical AI applications have access to the GPU, storage and compute resources they need. Finally, it is essential to continuously monitor utilization and cost. A FinOps mindset helps organizations optimize cloud spending based on the business value it delivers, with emphasis on visibility and accountability.
SCALABILITY: More than 70% of business leaders plan to scale both “AI factory” and AI edge deployments by 2028, and 61% expect to approximately double their token consumption by 2028. Yet many organizations face growth-related challenges, from skills gaps to financial uncertainty. Long-term planning is essential to prevent compatibility constraints and control costs as workloads grow. Modular data centers enable scalability, while hybrid and multicloud environments add flexibility as workload requirements evolve. To establish the right foundation, many organizations engage an expert partner that can help them forecast long-term needs and develop a plan for performance, resources and cost.
Click Below To Continue Reading
Accelerated compute uses GPUs and specialized accelerators to support AI workloads. A hybrid and modular approach, when paired with centralized management platforms, lets organizations start small and grow as needed while simplifying IT management. Accelerated compute strategies should align with workload needs and desired outcomes, whether cost savings or increased productivity, so that organizations receive the best cost per token.
• Modern CPUs, whether on-premises, in the cloud or at the edge, often have integrated AI engines and optimizations that are designed for performance, efficiency and scalability.
• General-purpose CPUs with built-in AI capabilities are generally sufficient for everyday workloads, inferences and analytics. These CPUs help organizations scale activities while managing costs and maintaining predictable performance.
• GPUs provide more compute for demanding AI workloads, such as model training, parallel processing and high-intensity inference. However, defaulting to GPUs or improperly configuring CPUs or GPUs often results in overinvestment.
• Other accelerators include tensor processing units, built for AI agents, machine learning and other specialized workloads, and neural processing units, which enable AI processing at the edge.
Multiple considerations should influence AI workload placement, including workload type, data volume and duration of model training. When organizations default to accelerated processors without identifying more nuanced requirements, they tend to overuse costly GPUs. Hybrid environments provide the flexibility to put workloads in the right place at the right time.
OPTIMIZING PERFORMANCE: Once workloads are defined, the next decision is where they should run. At a high level, the most important factors for AI performance are latency, data proximity and accessibility. Latency allows for rapid responses across the AI system. Proximity describes the physical distance between data and compute, and accessibility ensures the model can reach that data easily. One survey found that when developing average-sized AI models, 40% of organizations used AI-accelerated CPUs and 45% used GPUs or other accelerators in addition to CPUs. For large model development, those percentages jumped to 60% and 27%, respectively.
MODERN INFRASTRUCTURE: The cloud was originally thought to be the best environment for high-intensity workloads, but the normalization of these resource-heavy applications has quickly outdated that point of view. Inference workloads that require low latency or have high data gravity often perform best on-premises, and business are better equipped to maintain them that way. Historically, on-premises infrastructure was best for predictable, steady-state workloads where predictable scalability mitigated growth concerns. Organizations now know that keeping workloads on-premises for workloads that demand high performance allows for the ability to fine-tune or customize as needed.
Cloud environments provide short-term access to accelerated compute without interfering with routine processing. AI-enabled, customer-facing services are also well-suited to cloud environments that can accommodate uneven demand and ensure responsiveness across locations where latency is not a critical consideration.
WORKLOAD OPTIMIZATION: As organizations adapt their infrastructure to support AI, cloud rebalancing can be a smart move. Many enterprises are keeping elastic, variable and innovation-driven workloads in the cloud while pulling back those that are predictable, expensive or sensitive. When making repatriation decisions, organizations must ensure that on-premises infrastructure is sufficient to support AI workloads. They should also consider the costs of cloud egress and put measures in place to monitor costs as workloads expand or fluctuate.
SEAMLESS INTEROPERABILITY: Ultimately, an evolving set of criteria will influence where organizations place AI workloads today and in the future. Workload placement is a balancing act among competing priorities: cost considerations, operational complexity, latency and performance, data sovereignty, and compliance. It’s also important to note that these factors may vary considerably among organizations of different sizes and industries and even between use cases. Organizations that successfully navigate these trade-offs can achieve a more efficient, scalable and resilient AI infrastructure that delivers optimal performance without unnecessary cost or complexity.
Knowing when to leverage accelerated compute — and when not to — is fast becoming an essential organizational capability. Many organizations will achieve faster results, better performance and greater efficiency in their AI practices when they partner with experts who understand AI infrastructure and can help them build a roadmap for success.
TECHNOLOGY ALIGNMENT: As organizations progress on their AI journeys, many data center teams are increasing their fluency in aligning workloads with specific technologies to achieve the optimal mix of performance, latency and cost. Hybrid architectures are the key to having the right resources and the flexibility to shift between them. In general, GPUs deliver the accelerated compute required to train and develop large-scale models. Some CPUs are well suited for AI inferencing, AI-powered analytics and mixed workloads. While organizations often prioritize CPUs on the basis of cost, GPUs play a crucial role in improving performance for specific use cases.
CDW APPROACH: Controlling costs while optimizing performance can be challenging for teams that lack experience managing AI workloads or have not clearly aligned their AI initiatives with business objectives. CDW’s experts can help organizations at every stage of the journey: first, assessing workloads, data management and infrastructure needs; then designing hybrid architectures and deploying infrastructure across environments; and finally, preparing data, optimizing performance and managing costs over time.
CDW SERVICES: CDW’s services are designed to help organizations accelerate AI readiness through tailored engagements that focus on deriving value from infrastructure: successful data management strategies, optimized architecture and ongoing operational excellence. CDW’s services focus on reducing friction and improving outcomes, including:
• Data governance and ecosystem design workshops
• Data center modernization assessments encompassing virtual machines, storage, identity access management and networking
• GPU infrastructure design and integration
• AI infrastructure and model placement workshops
• Lifecycle support and managed services
OPERATIONAL ENABLEMENT: CDW can also help organizations deploy tools for operational enablement. These tools may provide secure, on-premises AI processing with the benefit of cloud-based management, allowing teams to seamlessly shift workloads for optimal results. They may serve as the bridge between cloud and on-premises environments, simplifying operations and enabling centralized control, policy and workload management.
BUSINESS OUTCOMES: Accelerated compute is a means to an end: better and more consistent performance, increased efficiency and reduced infrastructure bottlenecks. These outcomes tie directly to business objectives such as more effective cost control, faster time to insights and shortened time to market. Implemented strategically, accelerated compute helps organizations achieve results more quickly and improve their operational efficiency — in the data center, in the cloud and in the business.
- WORKLOAD-FIRST STRATEGY
- OPTIMIZE AI WORKLOAD PLACEMENT
- ENABLING AI OUTCOMES
When organizations understand their workloads across their full hybrid data center landscape, they can optimize infrastructure throughout the organization rather than analyzing their various business units’ workloads in silos. A holistic assessment allows for economies of scale and stronger cost controls by carefully matching AI workloads to the right compute resources.
START WITH OUTCOMES: Designing effective AI infrastructure starts by identifying the business outcomes that map to specific workloads and then to compute resources. Without these analyses, organizations risk investing in unneeded infrastructure or architecture that fails to deliver the required performance and the desired ROI. Drawing a clear line from outcomes to workloads and then compute ensures that workload requirements, data accessibility, latency and performance are aligned.
A COMPREHENSIVE PICTURE: As organizations’ AI adoption evolves and matures, so will the supporting workloads, and with that increased diversity of those workloads. This added complexity required a thorough evaluation of cost, ease of use, IT preferences, and skill sets within the business. Organizations are more likely to address these considerations when they design infrastructure for the entire portfolio rather than siloed use cases. Holistic planning improves performance, utilization, efficiency and economies of scale.
DATA CONSIDERATIONS: Data management is crucial for AI and a challenge for many organizations. Research indicates that 40% of financial institutions consider data-related issues their top AI challenge, and 84% of organizations across industries say their data is not fully optimized for AI and large language model ingestion. Data governance, integration and operations play an integral role in infrastructure modernization decisions. A comprehensive data management strategy will allow for businesses to ensure that their data has be properly prepared for the outcomes their business success demands, and by extension provide an informed approach when it comes to designing a productive and well-optimized hybrid data center environment.
CAPACITY PLANNING: Capacity planning requires a clear understanding of baseline workloads and how they will shift during periods of high intensity. While the cloud is the primary lever for scaling resources, it isn’t always an option; organizations may need to plan for future growth by prioritizing compatible, on-premises hardware.
Data center teams often benefit from workload scheduling and orchestration tools that allocate and coordinate workloads efficiently. Teams can also facilitate capacity planning — and ensure that mission-critical workloads are prioritized — by following defined policies for where to run production versus experimentation workloads.
Cloud rebalancing helps organizations optimize capacity planning for AI workloads by dynamically shifting compute resources across cloud environments based on demand, cost and performance requirements. This enables IT teams to avoid overprovisioning, reduce infrastructure costs, and ensure critical AI applications have access to the GPU, storage and compute resources they need. Finally, it is essential to continuously monitor utilization and cost. A FinOps mindset helps organizations optimize cloud spending based on the business value it delivers, with emphasis on visibility and accountability.
SCALABILITY: More than 70% of business leaders plan to scale both “AI factory” and AI edge deployments by 2028, and 61% expect to approximately double their token consumption by 2028. Yet many organizations face growth-related challenges, from skills gaps to financial uncertainty. Long-term planning is essential to prevent compatibility constraints and control costs as workloads grow. Modular data centers enable scalability, while hybrid and multicloud environments add flexibility as workload requirements evolve. To establish the right foundation, many organizations engage an expert partner that can help them forecast long-term needs and develop a plan for performance, resources and cost.
Click Below To Continue Reading
Accelerated compute uses GPUs and specialized accelerators to support AI workloads. A hybrid and modular approach, when paired with centralized management platforms, lets organizations start small and grow as needed while simplifying IT management. Accelerated compute strategies should align with workload needs and desired outcomes, whether cost savings or increased productivity, so that organizations receive the best cost per token.
• Modern CPUs, whether on-premises, in the cloud or at the edge, often have integrated AI engines and optimizations that are designed for performance, efficiency and scalability.
• General-purpose CPUs with built-in AI capabilities are generally sufficient for everyday workloads, inferences and analytics. These CPUs help organizations scale activities while managing costs and maintaining predictable performance.
• GPUs provide more compute for demanding AI workloads, such as model training, parallel processing and high-intensity inference. However, defaulting to GPUs or improperly configuring CPUs or GPUs often results in overinvestment.
• Other accelerators include tensor processing units, built for AI agents, machine learning and other specialized workloads, and neural processing units, which enable AI processing at the edge.
Multiple considerations should influence AI workload placement, including workload type, data volume and duration of model training. When organizations default to accelerated processors without identifying more nuanced requirements, they tend to overuse costly GPUs. Hybrid environments provide the flexibility to put workloads in the right place at the right time.
OPTIMIZING PERFORMANCE: Once workloads are defined, the next decision is where they should run. At a high level, the most important factors for AI performance are latency, data proximity and accessibility. Latency allows for rapid responses across the AI system. Proximity describes the physical distance between data and compute, and accessibility ensures the model can reach that data easily. One survey found that when developing average-sized AI models, 40% of organizations used AI-accelerated CPUs and 45% used GPUs or other accelerators in addition to CPUs. For large model development, those percentages jumped to 60% and 27%, respectively.
MODERN INFRASTRUCTURE: The cloud was originally thought to be the best environment for high-intensity workloads, but the normalization of these resource-heavy applications has quickly outdated that point of view. Inference workloads that require low latency or have high data gravity often perform best on-premises, and business are better equipped to maintain them that way. Historically, on-premises infrastructure was best for predictable, steady-state workloads where predictable scalability mitigated growth concerns. Organizations now know that keeping workloads on-premises for workloads that demand high performance allows for the ability to fine-tune or customize as needed.
Cloud environments provide short-term access to accelerated compute without interfering with routine processing. AI-enabled, customer-facing services are also well-suited to cloud environments that can accommodate uneven demand and ensure responsiveness across locations where latency is not a critical consideration.
WORKLOAD OPTIMIZATION: As organizations adapt their infrastructure to support AI, cloud rebalancing can be a smart move. Many enterprises are keeping elastic, variable and innovation-driven workloads in the cloud while pulling back those that are predictable, expensive or sensitive. When making repatriation decisions, organizations must ensure that on-premises infrastructure is sufficient to support AI workloads. They should also consider the costs of cloud egress and put measures in place to monitor costs as workloads expand or fluctuate.
SEAMLESS INTEROPERABILITY: Ultimately, an evolving set of criteria will influence where organizations place AI workloads today and in the future. Workload placement is a balancing act among competing priorities: cost considerations, operational complexity, latency and performance, data sovereignty, and compliance. It’s also important to note that these factors may vary considerably among organizations of different sizes and industries and even between use cases. Organizations that successfully navigate these trade-offs can achieve a more efficient, scalable and resilient AI infrastructure that delivers optimal performance without unnecessary cost or complexity.
Knowing when to leverage accelerated compute — and when not to — is fast becoming an essential organizational capability. Many organizations will achieve faster results, better performance and greater efficiency in their AI practices when they partner with experts who understand AI infrastructure and can help them build a roadmap for success.
TECHNOLOGY ALIGNMENT: As organizations progress on their AI journeys, many data center teams are increasing their fluency in aligning workloads with specific technologies to achieve the optimal mix of performance, latency and cost. Hybrid architectures are the key to having the right resources and the flexibility to shift between them. In general, GPUs deliver the accelerated compute required to train and develop large-scale models. Some CPUs are well suited for AI inferencing, AI-powered analytics and mixed workloads. While organizations often prioritize CPUs on the basis of cost, GPUs play a crucial role in improving performance for specific use cases.
CDW APPROACH: Controlling costs while optimizing performance can be challenging for teams that lack experience managing AI workloads or have not clearly aligned their AI initiatives with business objectives. CDW’s experts can help organizations at every stage of the journey: first, assessing workloads, data management and infrastructure needs; then designing hybrid architectures and deploying infrastructure across environments; and finally, preparing data, optimizing performance and managing costs over time.
CDW SERVICES: CDW’s services are designed to help organizations accelerate AI readiness through tailored engagements that focus on deriving value from infrastructure: successful data management strategies, optimized architecture and ongoing operational excellence. CDW’s services focus on reducing friction and improving outcomes, including:
• Data governance and ecosystem design workshops
• Data center modernization assessments encompassing virtual machines, storage, identity access management and networking
• GPU infrastructure design and integration
• AI infrastructure and model placement workshops
• Lifecycle support and managed services
OPERATIONAL ENABLEMENT: CDW can also help organizations deploy tools for operational enablement. These tools may provide secure, on-premises AI processing with the benefit of cloud-based management, allowing teams to seamlessly shift workloads for optimal results. They may serve as the bridge between cloud and on-premises environments, simplifying operations and enabling centralized control, policy and workload management.
BUSINESS OUTCOMES: Accelerated compute is a means to an end: better and more consistent performance, increased efficiency and reduced infrastructure bottlenecks. These outcomes tie directly to business objectives such as more effective cost control, faster time to insights and shortened time to market. Implemented strategically, accelerated compute helps organizations achieve results more quickly and improve their operational efficiency — in the data center, in the cloud and in the business.
CDW can help you deploy accelerated compute for better artificial intelligence outcomes.
Sana Gutierrez
Sr Mgr Cat & Brand Mgmt
Eryn Brodsky
Solution Practice Lead for Server and Storage
Mariano Carro
Principal Field Solution Architect