Chip comparison
NVIDIA A10 Tensor Core GPU vs NVIDIA A100 Tensor Core GPU
Side-by-side specs
Straight from each vendor's datasheet. Winner in bold: higher throughput, memory, or transistor count; lower power draw or price. Sparse (2:4) throughput roughly doubles the FP8/INT8 numbers shown.
| Field | NVIDIA A10 Tensor Core GPU | NVIDIA A100 Tensor Core GPU |
|---|---|---|
| Process node | Samsung 8N | TSMC N7 |
| Transistors (B) | 28.3 | 54 |
| Die size | 628 mm² | 826 mm² |
| TDP | 150 W | 400 W |
| Memory | 24 GB GDDR6 | 80 GB HBM2e |
| Memory bandwidth | 600 GB/s | 2,039 GB/s |
| FP16 (dense) | 125 TFLOPS | 312 TFLOPS |
| BF16 (dense) | 125 TFLOPS | 312 TFLOPS |
| INT8 (dense) | 250 TOPS | 624 TOPS |
| Launch price (list) | — | $10,000 |
| Form factor | PCIe | SXM4 |
| Announced | 2021-04-12 | 2020-05-14 |
| Released | 2021-04-12 | 2020-05-14 |
Robots running NVIDIA A10 Tensor Core GPU
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Robots running NVIDIA A100 Tensor Core GPU
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Data centers with NVIDIA A10 Tensor Core GPU
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Data centers with NVIDIA A100 Tensor Core GPU
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Common questions
NVIDIA A10 Tensor Core GPU vs NVIDIA A100 Tensor Core GPU: which is faster for training?
NVIDIA A100 Tensor Core GPU has 2.50x the dense FP16/BF16 throughput of the other (NVIDIA A10 Tensor Core GPU: 125 TFLOPS; NVIDIA A100 Tensor Core GPU: 312 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
NVIDIA A10 Tensor Core GPU vs NVIDIA A100 Tensor Core GPU: which has more memory?
NVIDIA A100 Tensor Core GPU carries more HBM (NVIDIA A10 Tensor Core GPU: 24 GB GDDR6; NVIDIA A100 Tensor Core GPU: 80 GB HBM2e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
NVIDIA A10 Tensor Core GPU vs NVIDIA A100 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: NVIDIA A10 Tensor Core GPU 0.83 (125 ÷ 150 W); NVIDIA A100 Tensor Core GPU 0.78 (312 ÷ 400 W). NVIDIA A10 Tensor Core GPU wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · NVIDIA A10 Tensor Core GPU full page · NVIDIA A100 Tensor Core GPU full page.