Chip comparison
NVIDIA V100 Tensor Core GPU 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 | NVIDIA V100 Tensor Core GPU | Tesla Dojo D1 |
|---|---|---|
| Process node | TSMC 12FFN | TSMC N7 |
| Transistors (B) | 21.1 | 50 |
| Die size | 815 mm² | 645 mm² |
| TDP | 300 W | 400 W |
| Memory | 32 GB HBM2 | — |
| Memory bandwidth | 900 GB/s | — |
| FP16 (dense) | 125 TFLOPS | — |
| BF16 (dense) | — | 362 TFLOPS |
| FP8 (dense) | — | 362 TFLOPS |
| Form factor | SXM2 / PCIe | Training tile (25 D1 chips per tile) |
| Announced | 2017-05-10 | 2021-08-19 |
| Released | 2017-12-01 | 2023-07-01 |
Robots running NVIDIA V100 Tensor Core GPU
No robots publicly running it yet.
Robots running Tesla Dojo D1
No robots publicly running it yet.
Data centers with NVIDIA V100 Tensor Core GPU
No data centers publicly running it yet.
Data centers with Tesla Dojo D1
No data centers publicly running it yet.
Common questions
NVIDIA V100 Tensor Core GPU vs Tesla Dojo D1: which is faster for training?
Tesla Dojo D1 has 2.90x the dense FP16/BF16 throughput of the other (NVIDIA V100 Tensor Core GPU: 125 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.
NVIDIA V100 Tensor Core GPU vs Tesla Dojo D1: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: NVIDIA V100 Tensor Core GPU 0.42 (125 ÷ 300 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). Tesla Dojo D1 wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · NVIDIA V100 Tensor Core GPU full page · Tesla Dojo D1 full page.