A100 40GB vs A100 80GB: What's the Difference?

Choose between A100 40GB and 80GB by the peak working set of the application, not the checkpoint size alone. The 80GB version can remove a memory constraint that forces quantization, CPU offload, or multiple GPUs on a 40GB device. If the same workload already fits comfortably, doubling capacity does not imply doubling speed.

Capacity, form factor, and bandwidth are separate choices

NVIDIA documents both 40GB and 80GB A100 variants. Its A100 specifications and product briefs distinguish PCIe from SXM: the 80GB PCIe specification lists 1,935 GB/s memory bandwidth, while the 80GB SXM specification lists 2,039 GB/s. These are hardware specifications, not application throughput. Record the exact variant instead of applying an SXM figure to any listing named A100.

Both capacities belong to the Ampere generation. More memory is not a new numerical format or a software upgrade. Check the chosen runtime's supported kernels, GPU count, host RAM, and storage as well as VRAM. A partitioned device, if offered, has its own visible memory budget; the physical card's label is not that budget.

A worked capacity screen

Assume a hypothetical dense model with exactly 13 billion parameters, all resident in BF16 at two bytes per parameter. Raw weights occupy 26 billion bytes, or about 24.2 GiB. Assume its measured runtime buffers require another 4 GiB and the intended request mix needs 8 GiB of cache. That produces a 36.2 GiB working-set estimate before an additional operating margin. The 4 GiB and 8 GiB figures are illustrative inputs, not measurements of a named model.

This is a borderline 40GB-class deployment, depending on device-visible capacity and transient peaks. It is a stronger reason to test 80GB than a generic performance ranking. Conversely, a measured 20 GiB peak with the required traffic might leave sufficient room on 40GB. Compare all budgets in one unit: GiB uses 2 to the power 30 bytes; vendor GB labels should not replace the runtime's reported bytes.

What extra memory can buy

• Longer requests: more token history can remain resident, provided the model supports the context and the service meets its latency target.
• More active sequences: additional cache capacity can help continuous batching. Queueing and compute still limit useful concurrency.
• A less compressed artifact: useful when a quantized model fails task-quality checks.
• Simpler placement: one larger GPU may avoid inter-GPU communication needed by a sharded model.

One 80GB GPU versus two 40GB GPUs

Two devices do not create one ordinary 80GB allocation. A supported parallel runtime must shard the model, and some tensors and buffers remain replicated. Confirm peer access and actual interconnect. If a model fits on one 40GB device and requests are independent, two replicas may be more useful than tensor parallelism. See the parallelism decision guide.

Verify before retaining the instance

nvidia-smi --query-gpu=name,memory.total,memory.free,driver_version --format=csv
nvidia-smi topo -m

These are read-only inventory queries, documented in NVIDIA SMI. Run them in the intended environment. Then test model loading, the longest admitted prompt plus output, and concurrent traffic. An OOM at loading suggests weights or initialization peaks; one during generation suggests cache or batching; one only on the first request may involve graph capture. Review the memory worksheet before changing allocation settings. Compare the exact current A100 offerings and follow instance lifecycle guidance for the evaluation.