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GPU → Does a dedicated server need a GPU?
Does a dedicated server need a GPU?
No, most dedicated servers do not need a GPU. Web hosting, databases, email, DNS, file storage, backups, VPN hosting, and most game servers run on CPU, RAM, and fast storage. You need a GPU for AI and machine learning, video transcoding, 3D rendering, scientific computing, and GPU-accelerated analytics.
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What is a GPU and what does it do?
A GPU, or graphics processing unit, was originally designed to render graphics and visual effects. These days, its high core count and parallel processing capabilities make it perfect for all kinds of compute-heavy workloads—far beyond just visuals.
CPUs are built for sequential tasks, while GPUs are specialized for parallel processing, making them work better for tasks that require performing many similar operations simultaneously, such as image processing, AI model training, and 3D rendering.
When a dedicated server does not need a GPU
A dedicated server doesn’t need a GPU if it never sends work to a graphics card or bottlenecks on parallel math.
You manage it without a screen. Dedicated servers run headless. You install the OS and administer the machine over SSH or through the baseboard management controller (BMC), which gives you a remote console and power control from a browser. The BMC has its own basic graphics chip, so you can reach the BIOS or watch an installer without adding a video card.
Integrated graphics cover the edge cases. If you ever plug in a monitor, onboard graphics handle it. The same goes for light media streaming: Plex and Jellyfin can use an Intel integrated GPU for hardware transcoding, so a small household library does not call for a discrete card.
A quick way to check: Search your software’s documentation for CUDA, NVENC, or GPU acceleration. If none of those appear as requirements or recommended settings, your workload runs on the CPU and the money is better spent on cores, RAM, or storage.
When dedicated servers do need a GPU
Some tasks benefit hugely from GPU acceleration. If you’re running any of these, a GPU-equipped server is worth serious consideration.
AI and machine learning workloads
Training deep learning models? Running real-time inferencing for NLP or computer vision? A GPU is non-negotiable. Frameworks like TensorFlow and PyTorch are optimized for CUDA, and newer cards (like the A100 or H100) offer tensor cores purpose-built for AI workloads.
Learn more about GPU servers for AI →
Video transcoding and media streaming
Media servers like Plex, Jellyfin, and Emby often transcode video in real time to match playback devices. A GPU—especially one with NVIDIA NVENC/NVDEC support—can handle these encoding/decoding jobs much faster than a CPU, while using less power.
Scientific computing and simulations
GPU acceleration is essential for high-performance computing (HPC) applications like molecular dynamics, fluid simulations, and other types of numerical modeling. CUDA or OpenCL-based apps can run 10x to 100x faster on a GPU than on CPU cores alone.
Game servers (sometimes)
Most dedicated game servers don’t need a GPU—Minecraft, Valheim, CS:GO, and similar games offload rendering to the client, not the server. But if you’re running a game client alongside the server (e.g. for local rendering, testing, or automation), a GPU becomes necessary.
Some Unity or Unreal games with headless clients may also require a GPU to run even in server mode.
Learn more about GPU servers for gaming →
Workstations used as servers
If you’re using your server as a remote desktop workstation—whether for video editing, 3D rendering, VFX work, or CAD—you’ll want to consider a GPU. Software like Blender, Adobe Premiere, or OctaneRender rely heavily on GPU acceleration.
Big data analytics
GPUs are increasingly used in big data environments to accelerate parallel processing across massive datasets. Platforms like RAPIDS, Spark with GPU support, and Dask can offload data transformations, aggregations, and machine learning tasks to GPUs—reducing runtime from hours to minutes in some cases.
If your stack involves heavy data crunching or real-time analytics, a GPU can seriously speed things up.
Learn more about GPU servers for data analytics →
Medical imaging
Medical imaging applications like MRI reconstruction, CT scan analysis, or 3D rendering for diagnostics rely on GPU compute power to process high-resolution data quickly and accurately. Frameworks like MONAI and Clara from NVIDIA are tailored to healthcare use cases, and GPU acceleration helps meet the low-latency demands of clinical workflows.
On-prem vs colocation vs renting: What’s your move?
If you’ve decided you need a GPU, your deployment options matter just as much:
? On-premises: This means purchasing and managing the server in your own facility. You get complete control over hardware, software, and security, which is ideal for long-term projects and organizations with in-house IT support.
But it’s capital-intensive, and you’re on the hook for power, cooling, physical space, and all maintenance.
? Colocation: You still own the server, but house it in a third-party data center. Colocation offers enterprise-grade infrastructure—reliable power, high-speed internet, climate control, and physical security—without having to build your own server room.
The tradeoff is that while you save on infrastructure, you’re still responsible for hardware maintenance and may need to pay for remote hands or travel to the site.
? Renting a dedicated GPU server: Ideal for teams that want flexibility, fast deployment, and predictable monthly costs. You lease the server from a provider, which eliminates CapEx and lets you swap or upgrade hardware easily.
This is a solid choice for startups, bursty GPU workloads, or projects where owning hardware doesn’t make financial or logistical sense.
Renting is ideal for:
- Pilot projects or short-term GPU workloads
- Avoiding CapEx
- Getting access to newer cards without buying
- Scaling quickly without infrastructure headaches
Next steps for choosing a dedicated server with a GPU
Most dedicated servers don’t need a GPU, but for GPU-accelerated tasks like AI, video streaming, or simulation-heavy workloads, it’s a game-changer. If your stack doesn’t touch graphics or parallel compute, stick with CPU.
If you’re building or upgrading your infrastructure, think carefully about workload needs before investing in GPU horsepower.
When you’re ready to rent a dedicated GPU server—or upgrade your server hosting—Liquid Web can help. Our dedicated server hosting options have been leading the industry for decades, because they’re fast, secure, and completely reliable. Choose your favorite OS and the management tier that works best for you.
Click below to explore GPU server options or start a chat to talk to one of our experts about GPU hosting!
Dedicated GPU server FAQs
Additional resources
What is a GPU? →
What is, how it works, common use cases, and more
GPU server vs workstation →
What’s the difference, what is each one best for, and how to decide what you need
What is GPU as a Service? →
Learn what it is and what it isn’t, how it compares to cloud GPU and bare metal GPU, and more
Amy Moruzzi is a Systems Engineer at Liquid Web with years of experience maintaining large fleets of servers in a wide variety of areas—including system management, deployment, maintenance, clustering, virtualization, and application level support. She specializes in Linux, but has experience working across the entire stack. Amy also enjoys creating software and tools to automate processes and make customers’ lives easier.