As enterprises increasingly adopt AI, data analytics, and immersive visual workloads, the performance of their compute infrastructure—especially GPUs—has never been more important. But not all GPU architectures serve the same purpose.
This guide breaks down the differences between integrated GPUs and dedicated GPUs—and more importantly, which fits best for enterprise use cases ranging from virtual desktop infrastructure (VDI) to machine learning inference at scale.
What’s an Integrated GPU?
Integrated GPUs are built into a system’s CPU and share system memory. They’re found in most laptops, mobile workstations, and entry-level servers. While power-efficient, they have limited processing capabilities and are not designed for high-throughput workloads.
Integrated GPUs are best for:
- Lightweight VDI environments
- Office productivity and video conferencing
- Low-cost, energy-efficient endpoints
- Field use with AI-enabled edge devices (e.g., integrated NPUs in ARM-based chips)
What’s a Dedicated GPU?
Dedicated GPUs are standalone processing units with their own high-bandwidth memory (VRAM) and compute resources. In the enterprise, they are essential for accelerating everything from 3D visualization to AI model training and real-time inferencing.
Dedicated GPUs are best for:
- High-performance AI/ML training and inference
- Data-intensive analytics and simulation
- Real-time 3D rendering and digital twins
- Virtual desktop infrastructure (VDI) for power users (e.g., CAD/CAM, media teams)
Integrated vs. Dedicated GPUs
| Feature | Integrated GPU | Dedicated GPU |
| Performance | Basic compute | High-throughput parallel compute |
| Power Efficiency | Very high | Depends on workload and cooling infrastructure |
| Use Case | Office productivity, light edge AI | AI/ML, analytics, rendering, VDI |
| Memory | Shared RAM | Dedicated VRAM |
| CapEx/OpEx | Lower cost, limited scale | Higher cost, scalable performance |
| Deployment | Edge devices, laptops | Data centers, AI clusters, VDI farms |
Beyond the Basics: Emerging GPU Architectures for the Enterprise
Today’s enterprises have options beyond just choosing between integrated and dedicated GPUs.
Consider:
- vGPUs (Virtual GPUs): Let multiple VMs share a physical GPU. Ideal for scaling VDI and design apps efficiently across teams. [Learn how Pure + NVIDIA vGPU accelerates VDI.]
- NPUs (Neural Processing Units): Power efficient AI accelerators often embedded in mobile/edge hardware. Good for edge inferencing and lightweight models.
- TPUs (Tensor Processing Units): Specialized chips optimized for tensor operations in machine learning (e.g., Google Cloud). Excellent for large-scale inference but may lack general-purpose flexibility.
Use Case Scenarios: What Your Enterprise Should Deploy
| Use Case | GPU Recommendation |
| Knowledge-worker VDI | Integrated GPU or vGPU |
| AI model training | High-end dedicated GPUs (e.g., NVIDIA H100) |
| Real-time AI inference (e.g., fraud detection) | Mid-range dGPUs or NPUs at the edge |
| Scientific modeling or simulation | Dedicated GPU servers with NVLink & high IOPS storage |
| Cloud-based analytics | Dedicated GPUs with scalable storage backplane |
The Storage Component: Why It Matters
Enterprise IT teams today must balance performance with sustainability. While integrated GPUs offer excellent energy savings, they fall short for compute-heavy tasks. Modern systems allow GPU switching, intelligently toggling between integrated and discrete GPUs to optimize power without compromising performance.
At the storage layer, Pure Storage provides high-performance, energy-efficient platforms that feed GPU workloads with fast, parallel access to massive data sets—critical for ML pipelines and real-time decisioning.
Even the fastest GPU is limited by the speed of its data pipeline.
- FlashBlade//S and FlashArray//X deliver the ultra-low latency and IOPS modern GPUs require.
- AIRI//S, Pure’s AI-ready infrastructure, pairs NVIDIA DGX systems with Pure Storage to accelerate time to insight.
- With Pure Fusion, you gain self-service, as-a-service delivery of GPU-powered storage across hybrid cloud environments.
Learn how Pure enables intelligent GPU utilization at scale.
Final Takeaways for Enterprise Decision-Makers
- Start with the workload: GPU choice should follow your application profile—integrated for lightweight tasks, dedicated for compute-intensive ones.
- Scale intelligently: Virtual GPUs and shared infrastructures allow scalable deployments for large teams without buying individual high-end systems.
- Don’t forget storage: Your GPU strategy is only as strong as the storage pipeline that feeds it.
- Think hybrid: From edge inferencing with NPUs to centralized AI model training on DGX + FlashBlade, hybrid GPU architectures are becoming the new standard.
Learn how to maximize GPU performance.
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