A100 SXM: 80 GB to push past your memory limit.

Why choose this card for your project

Go with the A100 SXM if the full job requires more than 48 GB and your software is compatible. Model loading, data and computation steps all need to fit into your estimate; 80 GB alone doesn't describe how much work you can get done.

• The memory requirement genuinely exceeds 48 GB.
• The software is compatible and your tests cover the full run, not just loading.
• The budget is compared against the expected result over 3, 7 or 30 days.

A budget example for your deliverable

To prepare a first AI deliverable whose needs exceed 48 GB, an A100 SXM batch over three days frames the cost of a representative run, along with its checks and its export.

For 1 lot of NVIDIA A100 SXM 80GB, that's 1 GPU for 3 days, the plan costs $120.00. This amount covers the entire period; preparation, client feedback and retrieval must fit within your schedule. The plan doesn't guarantee a computing duration.

The alternatives that change your decision

A40 · 48 GB : Reduce the rental total when 48 GB is enough for the work. The 3-day plan is $29.14 for 1 lot of 1 GPU.

H200 · 141 GB : Consider higher memory when 80 GB does not meet the identified need. The 3-day plan is $274.29 for 1 lot of 1 GPU.

Start from the failure you want to solve

Take the process that is running out of memory and keep its settings. Note what is loaded at the moment it stops: model, data, batch size, or input length. This way you can check whether the extra capacity solves the identified problem. Changing both the card and the settings at the same time would make that conclusion harder.

A loaded model doesn't represent the entire requirement

In an AI project, memory is also used for data and computation steps. A model that loads can still fail during use. Start with a short input, then try the longest cases you actually expect. If you reduce precision or batch size, also check the quality of the results.

Keep an understandable environment

The A100 uses the NVIDIA Ampere architecture. For a tool based on PyTorch, choose an installation compatible with your CUDA environment and verify that the GPU is recognized before loading the entire project. Keep a list of the installed versions. A small team saves time when a second person can redo the test with the same files and settings.

80 GB doesn't justify every change

If the work fits comfortably within 48 GB, the A40 lowers the cost of the period. The H100 PCIe and SXM keep 80 GB: their value must be measured against your application. To go beyond this capacity on a single NVIDIA card, consider the 141 GB H200 instead. So your current limit determines the useful comparison.

What to keep in mind before ordering

Prices cover the GPU plan and the entire period indicated. Network or wallet fees, CPU, system RAM, storage, transfers, licenses, localization, taxes and refund terms remain to be confirmed when they affect your purchase.

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Your plan, payable directly in crypto

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The useful references to prepare your choice

Specifications describe the hardware. Your software's results depend on the project and its configuration. Find the technical references and the guide suited to your decision.