Choosing a GPU for a server can get complicated quickly.

NVIDIA Tesla. Quadro. RTX. A-Series. L-Series. AMD Radeon Pro. Different amounts of VRAM, different power requirements, single-width cards, double-width cards—and that's before determining whether the GPU will actually work in your server.

The good news is that you don't have to choose based solely on model numbers.

The best place to start is with one simple question:

What are you trying to do with the GPU?

Whether you're building an AI server, virtualization host, rendering workstation, Plex server, or GPU-powered homelab, this guide will help you narrow down the right type of GPU and the Dell server or workstation to put it in.

Quick GPU Selection Guide

*GPU compatibility varies significantly by server configuration. Contact us before ordering a GPU-equipped server so we can verify compatibility.

Understanding GPU Families

NVIDIA Tesla P-Series

NVIDIA's Tesla P-Series remains an attractive option for customers building lower-cost GPU compute systems using refurbished hardware.

NVIDIA Tesla P4

Memory: 8GB GDDR5
Power: Approximately 75W
Form Factor: Low-profile, single-slot
Best For: Transcoding, inference, homelabs, light GPU compute

The Tesla P4 is especially interesting for customers who want GPU acceleration without installing a large, power-hungry card.

Its relatively low power consumption and compact design make it useful for media transcoding and lighter compute workloads.

Good applications:

• Plex transcoding
• Video processing
• AI inference
• Homelabs
• CUDA experimentation

NVIDIA Tesla P40

Memory: 24GB GDDR5
Power: Approximately 250W
Form Factor: Full-height, full-length, double-slot
Best For: AI experimentation, inference, virtualization and compute

The P40 provides substantially more GPU memory than the P4 and can be an appealing value for customers experimenting with larger AI models or GPU compute.

Because it is a passive enterprise GPU, however, it needs to be installed in a system designed to provide sufficient airflow.

Good applications:

• AI/ML experimentation
• GPU compute
• Inference
• Virtualization
• Large-memory CUDA workloads

NVIDIA Tesla P100

Memory: Commonly 12GB or 16GB HBM2
Best For: HPC, scientific computing and machine learning

The P100 was designed as a serious compute accelerator rather than a graphics card.

Its HBM2 memory and compute capabilities make it more appropriate for scientific and machine-learning workloads than applications requiring traditional workstation graphics.

NVIDIA Tesla V-Series

NVIDIA Tesla V100

The Tesla V100 represented a major advancement in NVIDIA's data-center GPU architecture and remains a capable accelerator for many compute workloads.

Common Memory Configurations: 16GB or 32GB HBM2
Best For: AI training, machine learning, HPC and scientific computing

The V100 introduced Tensor Cores designed specifically to accelerate machine-learning operations.

For customers looking for enterprise AI hardware without moving into the cost of newer A-Series GPUs, refurbished V100s can still be an interesting option.

Good applications:

• Machine learning
• Deep learning
• AI model training
• Scientific computing
• HPC
• CUDA development

NVIDIA T-Series

NVIDIA Tesla T4

Memory: 16GB GDDR6
Power: Approximately 70W
Form Factor: Low-profile, single-slot
Best For: AI inference, transcoding, VDI and efficient GPU compute

The T4 is one of the most versatile enterprise GPUs available for power-conscious applications.

Its combination of relatively low power consumption and 16GB of GPU memory makes it useful for a wide range of workloads.

Good applications:

• AI inference
• Plex/media transcoding
• Virtual desktop infrastructure
• Video processing
• CUDA workloads
• Homelabs

For customers who want useful GPU acceleration without adding several hundred watts of power consumption, the T4 can be an excellent choice.

NVIDIA A-Series GPUs

NVIDIA's A-Series GPUs offer a newer generation of enterprise acceleration and are particularly attractive for AI, virtualization and professional workloads.

NVIDIA A2

Memory: 16GB GDDR6
Power: Approximately 40–60W
Best For: AI inference, edge computing and low-power servers

The A2 is compact and efficient, making it useful when power consumption and physical space matter.

Best applications:

• AI inference
• Edge computing
• Light AI workloads
• VDI
• Low-power GPU acceleration

NVIDIA A10

Memory: 24GB GDDR6
Power: Approximately 150W
Best For: AI, graphics, VDI and mixed workloads

The A10 is an excellent middle-ground GPU.

It combines substantial VRAM with professional graphics and compute capability without requiring the extreme power consumption of larger accelerators.

Best applications:

• AI inference
• VDI
• Rendering
• Professional graphics
• CUDA
• Mixed compute workloads

NVIDIA A16

Memory: 64GB GDDR6 total
Best For: Virtual Desktop Infrastructure

The A16 was designed primarily for environments where many users need GPU-accelerated virtual desktops.

Rather than focusing primarily on one extremely powerful workload, it is designed to divide GPU resources across multiple virtual users.

Best applications:

• VMware Horizon
• Citrix
• VDI
• Multi-user virtual workstations

NVIDIA A30 / A40

These GPUs move further into serious data-center compute.

A30

Memory: 24GB HBM2
Best For: AI, machine learning and HPC

A40

Memory: 48GB GDDR6
Best For: AI, rendering, visualization and virtual workstations

The A40 is particularly interesting when you need both substantial GPU memory and professional visualization capabilities.

NVIDIA Quadro and RTX Professional GPUs

Not every GPU workload belongs in a rack server.

For CAD, engineering, architecture, video production and local 3D applications, a Dell Precision workstation may actually be the better choice.

NVIDIA Quadro and newer RTX professional cards are designed for professional graphics workloads.

Depending on the workstation generation, available options may include cards from families such as:

• Quadro P-Series
• Quadro RTX
• RTX A-Series
• RTX professional GPUs

Best Uses for Professional RTX GPUs

These cards are particularly well suited for:

• AutoCAD
• SolidWorks
• Revit
• 3D modeling
• Engineering
• Architecture
• Rendering
• Video editing
• Content creation
• Local AI development

If you need to sit at the computer and use the GPU for graphics output, a Precision workstation will often make more sense than a PowerEdge rack server.

How Much VRAM Do You Need?

VRAM—or GPU memory—is one of the most important specifications to consider.

As a general starting point:

8GB or less:
Transcoding, basic GPU acceleration, lightweight CUDA and entry-level workloads.

12GB–16GB:
AI inference, development, moderate rendering and more demanding compute.

24GB:
Larger AI workloads, rendering, professional applications and heavier compute.

32GB–48GB+:
Large datasets, AI/ML, enterprise visualization, complex rendering and advanced GPU compute.

80GB and beyond:
Large-scale AI models, advanced HPC and enterprise AI infrastructure.

More VRAM isn't automatically better.

If your application only requires 8GB, buying a 48GB enterprise GPU may provide little benefit. Your software and dataset should determine how much GPU memory you actually need.

Which Dell Server Should You Use?

Dell PowerEdge R730

Best for: Budget GPU builds

The R730 is an excellent starting point when price matters.

It can support substantial GPU hardware when properly configured and is especially attractive for:

• Homelabs
• CUDA development
• Rendering
• Older Tesla GPUs
• GPU passthrough
• Research environments

For customers trying to get maximum compute capability per dollar, the R730 remains a compelling platform.

Dell PowerEdge R740

Best for: Overall price-to-performance

The R740 is our sweet spot for many GPU customers.

Its newer Xeon Scalable platform, strong memory capability and flexible PCIe configuration make it a great foundation for:

• AI
• Machine learning
• VDI
• Rendering
• GPU virtualization
• Scientific computing
• Professional compute

For many customers shopping refurbished hardware, we'd start here.

Dell PowerEdge R750

Best for: Newer enterprise GPU workloads

The R750 provides a newer platform and support for newer generations of data-center GPUs when properly configured.

It's a better starting point for customers building:

• Production AI systems
• Machine-learning servers
• Enterprise VDI
• Data analytics
• Newer GPU compute environments

The R750 is particularly attractive when you want to invest in newer GPU technology while still taking advantage of refurbished enterprise hardware.

Dell Precision Workstations

Best for: Professional graphics and desktop GPU workloads

If you need a powerful GPU but don't need a rack server, don't overlook Dell Precision.

Precision Towers can be an excellent platform for:

• CAD
• Engineering
• Architecture
• Video editing
• 3D rendering
• Content creation
• Local AI development
• Professional graphics

They're also far more practical than a rack server in an office or home environment where noise matters.

Important: GPU Compatibility Depends on the Server Configuration

This is where buying a GPU server gets more complicated.

An available PCIe slot does not automatically mean a GPU is compatible.

Depending on the PowerEdge model and GPU, the system may require:

• GPU enablement kit
• Specific PCIe risers
• GPU power cables
• Higher-wattage power supplies
• Different heatsinks
• High-performance fans
• GPU air shrouds
• Two processors
• Specific drive/chassis configurations

GPU size and power consumption also matter.

Some cards are low-profile and consume less than 75 watts, while large enterprise GPUs can occupy two PCIe slots and consume several hundred watts.

That's why we strongly recommend contacting us before purchasing a server for a specific GPU.

Special-Order GPUs Are Available

Don't see the GPU you need listed with one of our servers?

We can source most GPUs by special order.

Tell us which GPU you need—or simply tell us what you're trying to accomplish—and we can help determine the appropriate server, GPU, power, riser and cooling configuration.

Please Allow Additional Build Time

Because many GPUs are sourced specifically for individual customer builds, special-order GPU configurations may require additional processing and build time.

Lead times depend on GPU availability and the hardware required to properly configure the server.

If you're working with a deployment deadline, contact us before ordering so we can discuss current availability and estimated build time.

Still Not Sure Which GPU You Need?

That's okay.

You don't have to be a GPU expert to order a GPU server.

Tell us:

What software are you running?
What are you trying to accomplish?
What is your approximate budget?
Do you need a rack server or workstation?
Do you already have a GPU in mind?

From there, we can help you determine which platform makes the most sense.

Whether you're building an affordable R730 homelab, an R740 GPU compute server, an R750 AI platform, or a Precision workstation for professional graphics, we'll help you build a system around the workload—not just a list of specifications.

Need a custom GPU server? Contact us for a configuration and quote.