Chip comparison
Google TPU v5p 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 v5p | Tesla Dojo D1 |
|---|---|---|
| Process node | — | TSMC N7 |
| Transistors (B) | — | 50 |
| Die size | — | 645 mm² |
| TDP | 300 W | 400 W |
| Memory | 95 GB HBM2e | — |
| Memory bandwidth | 2,765 GB/s | — |
| BF16 (dense) | 459 TFLOPS | 362 TFLOPS |
| FP8 (dense) | — | 362 TFLOPS |
| INT8 (dense) | 918 TOPS | — |
| Form factor | OAM (per-chip in v5p pod) | Training tile (25 D1 chips per tile) |
| Announced | 2023-12-06 | 2021-08-19 |
| Released | 2024-01-01 | 2023-07-01 |
Robots running Google TPU v5p
No robots publicly running it yet.
Robots running Tesla Dojo D1
No robots publicly running it yet.
Data centers with Google TPU v5p
Data centers with Tesla Dojo D1
No data centers publicly running it yet.
Common questions
Google TPU v5p vs Tesla Dojo D1: which is faster for training?
Google TPU v5p has 1.27x the dense FP16/BF16 throughput of the other (Google TPU v5p: 459 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 v5p vs Tesla Dojo D1: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Google TPU v5p 1.53 (459 ÷ 300 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). Google TPU v5p wins at the die level; system-level efficiency also depends on cooling and interconnect.
Google TPU v5p vs Tesla Dojo D1: which is more widely used?
Google TPU v5p: 0 robots and 1 data center publicly running it. Tesla Dojo D1: 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 v5p full page · Tesla Dojo D1 full page.