Accelerated Computing
Accelerate AI with the Right Compute Strategy
CDW helps organizations modernize CPU and GPU platforms to deliver AI performance, efficiency, and control—minimizing disruptive data center overhauls and achieving faster time to first token.
Infrastructure That Supports AI
AI success starts with the right infrastructure. Learn how aligning accelerated compute resources to specific workloads can improve performance, scalability and cost efficiency.
Why Accelerated Compute Matters for AI Workloads
Traditional data center refresh models weren’t designed for AI workloads. Treating AI like a standard refresh can lead to overinvestment, misaligned architectures, and underutilized resources.
AI Workloads Require Tailored Solutions
AI workloads vary widely in intensity and design. Some benefit from GPU acceleration, while many run efficiently on modern CPUs with built-in AI engines. Treating all AI the same leads to misaligned architectures and unnecessary cost.
Accelerated Compute Strategies Often Require Distributed Placement Across Cloud, Data Center, and Edge Environments
Latency, data gravity, and compliance often require AI workloads to run outside the cloud. Accelerated compute enables AI inference and analytics to run on-prem, in hybrid environments, or at the edge—where performance and control matter most.
Overaccelerating Drives Unnecessary Spend
Defaulting to GPUs or mismatched CPU/GPU configurations often leads to overinvestment and underutilized resources. The right acceleration strategy aligns compute to workload intent—delivering performance without excess complexity.
Enterprise AI initiatives often achieve better ROI by aligning infrastructure to workload needs rather than overbuilding capacity.
Choose the Right Acceleration Path
CDW brings together silicon, platforms, and validated server designs to deliver accelerated AI compute—without expanding scope beyond what workloads require.
AMD EPYC™ processors provide high core density and memory bandwidth to accelerate data-intensive and scale-out AI workloads. Designed for throughput and performance-per-watt where efficient CPU-driven acceleration matters most.
Select Intel Xeon 6 processors include built-in AI acceleration technologies such as AMX for inference optimization for inference, analytics, and mixed workloads, enabling organizations to run AI efficiently on CPUs. Ideal for scaling everyday AI workloads with predictable performance and cost control.
NVIDIA’s accelerated computing platform delivers powerful GPU performance for the most demanding AI workloads, including large-scale training and high-intensity inference, where massive parallel processing is required.
Validated Platforms for Accelerated Compute
Validated designs. GPU support. Security, lifecycle services, and scalability.
Cisco provides high-performance AI infrastructure from data center to edge using validated, integrated systems. These scalable platforms enable GPU-powered training and inference while delivering enterprise-grade security and lifecycle services.
Dell PowerEdge platforms—including the PowerEdge XE series—are purpose-built for accelerated computing, supporting dense CPU and GPU configurations for AI training and inference. Modular designs help organizations scale AI acceleration without overbuilding the data center.
HPE delivers accelerated compute through platforms like HPE ProLiant and HPE Cray systems, engineered for CPU- and GPU-dense AI workloads. These validated designs support high-performance acceleration with enterprise-grade security, cooling, and lifecycle services.
Azure Local: Where Accelerated Compute Lives
Azure Local unifies on-prem, hybrid, and edge environments so AI workloads run where performance, latency, and data sovereignty demand — all managed through one Azure consistent control plane.
Local Execution for AI Workloads
Run inference, analytics, and data‑intensive AI workloads close to your data for lower latency and better performance.
Unified Management with Azure Arc
A single control plane for policy, security, and lifecycle management across datacenter, hybrid, and edge environments.
CDW Validated Hybrid Architecture
CDW delivers OEM-certified and validated designs… that align your silicon + server choices like Azure Local or any other HCI platform.
Accelerated Compute Readiness Checklist
AI workloads demand the right infrastructure strategy. This checklist helps you assess your organization's readiness for accelerated compute, including workload placement, infrastructure planning and operational scalability across hybrid environments.
Assess your readiness for accelerated compute and prepare your infrastructure to support AI growth.
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Your Accelerated Compute Strategy Starts Here
Whether you’re enabling AI inference, scaling data-intensive workloads, or modernizing compute for hybrid execution, CDW helps you choose the right acceleration path—minimizing disruptive data center overhauls and achieving faster time to first token.