Choose a CPU-only server if your application does not support GPU acceleration or CPU performance already meets your workload’s needs. Consider a GPU server when your software can use GPU parallelism for work such as deep-learning training or inference, selected high-performance computing, rendering, or video analytics—and the performance benefit justifies the added hardware and operating requirements.
The deciding factor is not the server label: it is whether the complete system can run your actual workload efficiently. That means checking application support, data and model size, throughput or latency goals, and the CPU, memory, storage, networking, power, and cooling around the GPU.
What is the difference between a GPU server and a CPU server?
A CPU-only server relies on its central processing unit to run applications. A GPU server adds one or more graphics processing units that can accelerate workloads designed to perform many suitable calculations in parallel. Adding a GPU does not automatically speed up every application: the software must support it, and the rest of the system must keep it supplied with data.
For that reason, “GPU versus CPU” is not a choice between a capable server and an incapable one. It is a choice about which hardware fits a particular application and workload. NVIDIA lists AI inference and training, high-performance computing (HPC), rendering and virtual workstations, virtual desktop infrastructure (VDI), cloud gaming, and intelligent video analytics as GPU-server use cases, but support and benefits vary by application. See NVIDIA’s NVIDIA-Certified Systems Configuration Guide for its workload and configuration guidance.
#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
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When should you choose a GPU server?
Deep-learning training
Training can benefit from GPUs when the training software supports them and the workload can use their parallel processing capacity. The system also needs enough host CPU capacity to prepare data, sufficient system memory, and storage capable of feeding the training pipeline. A GPU can sit underused if preprocessing or data access is the bottleneck. NVIDIA’s deep-learning training server guidance discusses those supporting requirements.
AI inference
Inference means using a trained model to produce results. GPU acceleration may suit inference workloads with demanding throughput or latency targets, but the right setup depends on the model, request volume, concurrency, and deployment location. A data-center inference server and an edge device with tighter power and space limits can have different requirements; NVIDIA covers that distinction in its inference server guidance.
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
Selected HPC, rendering, and video workloads
Some HPC calculations, rendering applications, and video analytics tools can use GPUs effectively. The category alone is not enough to justify buying one: verify that the specific application version supports the proposed GPU and software stack, then establish whether acceleration helps with your data and performance targets.
When is a CPU-only server the better choice?
- Your application does not support GPU acceleration, or its supported workflow does not use the GPU you are considering.
- CPU-only execution already meets your throughput and latency requirements.
- The workload is small or intermittent enough that dedicated GPU hardware and its operating needs are not justified.
- Your deployment cannot accommodate the GPU system’s power, cooling, space, or infrastructure requirements.
CPU infrastructure remains a valid option for inference and other applications; the decision should follow workload and system fit rather than a blanket assumption that GPUs are always faster. NVIDIA’s inference guidance describes both CPU- and GPU-based infrastructure options.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
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- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
How to decide which server you need
- Name the application and verify support. Check the documentation for the exact application version, GPU model, drivers, libraries, and other required software. Do not assume that a product category such as “AI” or “rendering” guarantees GPU support.
- Describe the real workload. Record representative data or model size, expected concurrency, and the throughput or latency target. These details help distinguish a GPU-suitable workload from one that is merely described as compute-intensive.
- Establish a CPU-only baseline. Use representative measurements or the software vendor’s documented requirements to see whether CPU execution is adequate. A vendor benchmark for a different model, batch size, or system is not a universal speedup estimate.
- Check whether the GPU can be kept busy. Consider data preparation, preprocessing, storage performance, and movement between system memory and GPU memory. Confirm that the model or working set fits the available GPU memory and that the host has enough memory for the rest of the pipeline.
- Size the host and deployment together. Account for CPU resources, system memory, GPU count and memory, PCIe lanes and topology, storage, networking, power, cooling, physical space, and deployment location. NVIDIA’s certified-system guide offers recommendations for particular configurations; they are starting points for those deployments, not universal minimum requirements.
- Compare ownership with alternatives. If the need is variable or temporary, compare buying with upgrading an existing compatible server or renting GPU compute. Use your own utilization, data-transfer needs, latency, privacy requirements, deployment constraints, and regional costs; there is no general break-even figure that fits every workload.
What to compare before committing
| Decision factor | What to establish |
|---|---|
| Application fit | Whether the exact application and software stack support the proposed GPU, or whether CPU execution is the relevant baseline. |
| Performance target | The required throughput or latency, at representative data sizes, batch sizes, or concurrency—not an unrelated benchmark’s speedup. |
| Memory and data movement | Whether model or dataset requirements fit GPU and host memory, and whether preprocessing and storage can supply data effectively. |
| Scale and interconnect | Whether the job needs one GPU, several GPUs in one server, or multiple nodes; include PCIe topology and networking in the design. |
| Operations | Available power, cooling, physical or rack space, support, and whether the deployment is in a data center or at the edge. |
| Economics and utilization | Expected useful workload over time, purchase and operating costs, and the practical alternatives of existing hardware or rented compute. |
Can you upgrade an existing CPU server?
Possibly, but compatibility is specific to the platform. Before adding a GPU, confirm that the server supports the card’s physical dimensions and power requirements, has suitable cooling and PCIe connectivity, and is compatible with the required software. For a CPU upgrade, check the socket, motherboard, firmware, memory, cooling, and PCIe compatibility. Neither a GPU nor a faster CPU should be chosen from workload labels alone; verify the complete configuration with the system vendor.
Quick Recap
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
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