Chip comparison
Google TPU v5e 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 | Google TPU v5e | NVIDIA A100 Tensor Core GPU |
|---|---|---|
| Process node | — | TSMC N7 |
| Transistors (B) | — | 54 |
| Die size | — | 826 mm² |
| TDP | 170 W | 400 W |
| Memory | 16 GB HBM2 | 80 GB HBM2e |
| Memory bandwidth | 819 GB/s | 2,039 GB/s |
| FP16 (dense) | — | 312 TFLOPS |
| BF16 (dense) | 197 TFLOPS | 312 TFLOPS |
| INT8 (dense) | 393 TOPS | 624 TOPS |
| Launch price (list) | — | $10,000 |
| Form factor | OAM (v5e pod, up to 256 chips) | SXM4 |
| Announced | 2023-08-29 | 2020-05-14 |
| Released | 2023-11-01 | 2020-05-14 |
Robots running Google TPU v5e
No robots publicly running it yet.
Robots running NVIDIA A100 Tensor Core GPU
No robots publicly running it yet.
Data centers with Google TPU v5e
Data centers with NVIDIA A100 Tensor Core GPU
No data centers publicly running it yet.
Common questions
Google TPU v5e vs NVIDIA A100 Tensor Core GPU: which is faster for training?
NVIDIA A100 Tensor Core GPU has 1.58x the dense FP16/BF16 throughput of the other (Google TPU v5e: 197 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.
Google TPU v5e vs NVIDIA A100 Tensor Core GPU: which has more memory?
NVIDIA A100 Tensor Core GPU carries more HBM (Google TPU v5e: 16 GB HBM2; 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.
Google TPU v5e vs NVIDIA A100 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Google TPU v5e 1.16 (197 ÷ 170 W); NVIDIA A100 Tensor Core GPU 0.78 (312 ÷ 400 W). Google TPU v5e wins at the die level; system-level efficiency also depends on cooling and interconnect.
Google TPU v5e vs NVIDIA A100 Tensor Core GPU: which is more widely used?
Google TPU v5e: 0 robots and 1 data center publicly running it. NVIDIA A100 Tensor Core GPU: 0 robots and 0 data centers. Empty here means no operator has said publicly, not zero adoption in the market.
See also: every chip comparison · Google TPU v5e full page · NVIDIA A100 Tensor Core GPU full page.