Server and AI accelerators are data centre graphics cards built for machine learning, inference, simulation and other heavy parallel computing in servers and workstations. The range is small and aimed at businesses, research teams and IT resellers, with products from NVIDIA, PNY, Dell and AMD.
How to choose a server or AI accelerator
Workload. Training a large model, running inference on a trained one and scientific computing all stress a card differently. Training needs the most memory and compute, while inference often runs well on a more modest accelerator, so define the job before comparing models.
Memory capacity. The memory on the card decides whether your model and its data fit at all. Bandwidth affects how quickly the card is fed, and many data centre cards also offer ECC memory, which corrects memory errors during long runs.
Software support. Most AI frameworks are built first around NVIDIA's CUDA, which makes setup straightforward. AMD accelerators use the ROCm platform, so check that your framework, libraries and operating system support it before you buy.
Cooling and form factor. Many server accelerators have no fan and rely on strong airflow from the server chassis. Fitting one in a standard workstation or desktop case can lead to overheating, so check the cooling type, slot width and card length against your enclosure.
Power and server compatibility. These cards draw far more power than typical office graphics cards and often need dedicated power connectors. Confirm the PCIe generation, the power supply capacity and, for branded servers, whether the manufacturer lists the card as supported.
Server and AI accelerator brands we carry
- NVIDIA: the company behind the CUDA platform, which most AI software is written for, and the largest brand in this category in our range.
- PNY: a long-standing NVIDIA partner that supplies NVIDIA professional and data centre boards to resellers and system builders.
- Dell: a server manufacturer, so its accelerators are aimed at customers who want parts that match PowerEdge servers.
- AMD: known for the Instinct accelerator line and the open ROCm software stack, which gives buyers an alternative to CUDA.
Server and AI accelerator FAQ
What is the difference between an AI accelerator and a gaming graphics card?
An AI accelerator is designed for continuous computing workloads rather than games. It usually has more memory, error-correcting memory and drivers aimed at servers. Many models have no video outputs at all and are used purely for calculation.
Can I install a server GPU in a normal desktop PC?
Sometimes, but it is not always sensible. Passively cooled cards need server-style airflow, and they can demand more power and a compatible motherboard than a desktop provides. Check the cooling design, power connectors and PCIe slot before buying.
How much GPU memory do I need for AI?
It depends on the size of the model and the batch of data you process. As a rule, the model's weights must fit in the card's memory with room to spare, and training needs considerably more than inference. If you are unsure, check the memory requirements published for the model you plan to run.
Should I choose NVIDIA or AMD for AI work?
NVIDIA is the safer choice if you rely on widely used frameworks and tutorials, because CUDA is supported almost everywhere. AMD can be a good fit if your software already supports ROCm. Check compatibility for your specific tools first.
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