Chip comparison
Google TPU v7 (Ironwood) 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 v7 (Ironwood) | NVIDIA A100 Tensor Core GPU |
|---|---|---|
| Process node | — | TSMC N7 |
| Transistors (B) | — | 54 |
| Die size | — | 826 mm² |
| TDP | 700 W | 400 W |
| Memory | 192 GB HBM3e | 80 GB HBM2e |
| Memory bandwidth | 7,400 GB/s | 2,039 GB/s |
| FP16 (dense) | — | 312 TFLOPS |
| BF16 (dense) | 4,614 TFLOPS | 312 TFLOPS |
| FP8 (dense) | 9,228 TFLOPS | — |
| INT8 (dense) | — | 624 TOPS |
| Launch price (list) | — | $10,000 |
| Form factor | OAM (Ironwood pod, up to 9,216 chips) | SXM4 |
| Announced | 2025-04-09 | 2020-05-14 |
| Released | 2025-12-01 | 2020-05-14 |
Robots running Google TPU v7 (Ironwood)
No robots publicly running it yet.
Robots running NVIDIA A100 Tensor Core GPU
No robots publicly running it yet.
Data centers with Google TPU v7 (Ironwood)
No data centers publicly running it yet.
Data centers with NVIDIA A100 Tensor Core GPU
No data centers publicly running it yet.
Common questions
Google TPU v7 (Ironwood) vs NVIDIA A100 Tensor Core GPU: which is faster for training?
Google TPU v7 (Ironwood) has 29.58x the dense FP16/BF16 throughput of the other (Google TPU v7 (Ironwood): 9,228 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 v7 (Ironwood) vs NVIDIA A100 Tensor Core GPU: which has more memory?
Google TPU v7 (Ironwood) carries more HBM (Google TPU v7 (Ironwood): 192 GB HBM3e; 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 v7 (Ironwood) vs NVIDIA A100 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Google TPU v7 (Ironwood) 13.18 (9,228 ÷ 700 W); NVIDIA A100 Tensor Core GPU 0.78 (312 ÷ 400 W). Google TPU v7 (Ironwood) wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Google TPU v7 (Ironwood) full page · NVIDIA A100 Tensor Core GPU full page.