Chip comparison
Google TPU v7 (Ironwood) vs Tesla Dojo D1
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) | Tesla Dojo D1 |
|---|---|---|
| Process node | — | TSMC N7 |
| Transistors (B) | — | 50 |
| Die size | — | 645 mm² |
| TDP | 700 W | 400 W |
| Memory | 192 GB HBM3e | — |
| Memory bandwidth | 7,400 GB/s | — |
| BF16 (dense) | 4,614 TFLOPS | 362 TFLOPS |
| FP8 (dense) | 9,228 TFLOPS | 362 TFLOPS |
| Form factor | OAM (Ironwood pod, up to 9,216 chips) | Training tile (25 D1 chips per tile) |
| Announced | 2025-04-09 | 2021-08-19 |
| Released | 2025-12-01 | 2023-07-01 |
Robots running Google TPU v7 (Ironwood)
No robots publicly running it yet.
Robots running Tesla Dojo D1
No robots publicly running it yet.
Data centers with Google TPU v7 (Ironwood)
No data centers publicly running it yet.
Data centers with Tesla Dojo D1
No data centers publicly running it yet.
Common questions
Google TPU v7 (Ironwood) vs Tesla Dojo D1: which is faster for training?
Google TPU v7 (Ironwood) has 25.49x the dense FP8 throughput of the other (Google TPU v7 (Ironwood): 9,228 TFLOPS; Tesla Dojo D1: 362 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 Tesla Dojo D1: which is more power-efficient?
Dense FP8 TFLOPS per watt: Google TPU v7 (Ironwood) 13.18 (9,228 ÷ 700 W); Tesla Dojo D1 0.91 (362 ÷ 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 · Tesla Dojo D1 full page.