NVIDIA A100 80GB
Accelerator profile with 80 GB HBM2e, 6,912 CUDA cores, and 19.5 FP32 TFLOPS.
- VRAM
- 80 GB
- CUDA cores
- 6,912
- FP32
- 19.5
- TDP
- 400 W
Benchmarks
Sample benchmark scores are rendered as horizontal ECharts bars with an HTML fallback table.
| Geekbench OpenCL | 214,000 |
|---|---|
| Blender BMW | 42 s |
Plans with this GPU
Sample GPU tariffs using this accelerator, sorted by monthly price in the active currency.
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| Plan | Provider | GPU | CPU | RAM | Location | Price | Action |
|---|---|---|---|---|---|---|---|
| No sampled plans for this GPU yet. | |||||||
Compare
GPU-to-GPU comparison pages will attach here when comparison content exists.
What the NVIDIA A100 80GB is good for
NVIDIA A100 80GB is an Ampere data-center GPU for large AI training jobs, memory-heavy inference, and batch compute where VRAM matters more than headline FP32 speed. It fits teams that need 80 GB HBM2e on one accelerator and can justify SXM4 server pricing.
- NVIDIA A100 80GB uses 80 GB of HBM2e memory.
- NVIDIA A100 80GB exposes 6,912 CUDA cores and 432 Tensor cores.
- NVIDIA A100 80GB has a 400 W TDP, so power density is part of the hosting bill.
- NVIDIA A100 80GB uses the SXM4 interface, not a simple PCIe card slot.
Do not buy an A100 plan just because the model name looks serious. The real bill is the monthly GPU price plus CPU allocation, RAM, NVMe storage, snapshots, backups, egress traffic, and the time needed to move large datasets in and out. For US and UK teams paying in USD, the network line item can hurt more than the advertised GPU hour.
Where it makes sense
NVIDIA A100 80GB is a strong fit for transformer training, fine-tuning, retrieval systems with large embeddings, and inference workloads that fail on 24 GB or 48 GB cards. The 80 GB memory pool is the point. If your model fits comfortably on an NVIDIA A10 24GB or NVIDIA L40S 48GB, the A100 premium needs a hard reason.
The A100 80GB delivers 312 FP16 TFLOPS in the specification table. The same table lists 19.5 FP32 TFLOPS and 2,039 GB/s of memory bandwidth. Those numbers favor tensor-heavy AI workloads and memory movement. They do not make the card a cheap general-purpose render box.
Hosting checks before you rent it
Check the GPU count first. A listing with one A100 80GB is not the same product as a multi-GPU node with high-speed interconnects, even when both pages show the same accelerator name. Check whether the plan is bare metal, a virtual machine, or a shared GPU slice. The performance and blast radius are different.
Check the CPU and RAM ratio next. A 400 W accelerator paired with weak host CPUs can sit idle while the job waits on preprocessing, storage, or network transfer. For training and fine-tuning, ask whether NVMe storage is local, how snapshots are billed, and whether outbound traffic has a fair-use limit.
Treat benchmark bars on this page as directional until source attribution is published. The useful production decision is simpler: choose A100 80GB when memory capacity blocks the job, choose L40S when newer Ada throughput and PCIe deployment matter, and choose A10 when price discipline matters more than peak AI throughput.