DEPLOYDatabase

Chip comparison

NVIDIA H200 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.

FieldNVIDIA H200 Tensor Core GPUTesla Dojo D1
Process nodeTSMC 4NTSMC N7
Transistors (B)8050
Die size814 mm²645 mm²
TDP700 W400 W
Memory141 GB HBM3e
Memory bandwidth4,800 GB/s
FP16 (dense)989 TFLOPS
BF16 (dense)989 TFLOPS362 TFLOPS
FP8 (dense)1,979 TFLOPS362 TFLOPS
INT8 (dense)1,979 TOPS
Form factorSXM5Training tile (25 D1 chips per tile)
Announced2023-11-132021-08-19
Released2024-03-012023-07-01

Robots running NVIDIA H200 Tensor Core GPU

No robots publicly running it yet.

Robots running Tesla Dojo D1

No robots publicly running it yet.

Data centers with Tesla Dojo D1

No data centers publicly running it yet.

Common questions

NVIDIA H200 Tensor Core GPU vs Tesla Dojo D1: which is faster for training?

NVIDIA H200 Tensor Core GPU has 5.47x the dense FP8 throughput of the other (NVIDIA H200 Tensor Core GPU: 1,979 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 H200 Tensor Core GPU vs Tesla Dojo D1: which is more power-efficient?

Dense FP8 TFLOPS per watt: NVIDIA H200 Tensor Core GPU 2.83 (1,979 ÷ 700 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). NVIDIA H200 Tensor Core GPU wins at the die level; system-level efficiency also depends on cooling and interconnect.

NVIDIA H200 Tensor Core GPU vs Tesla Dojo D1: which is more widely used?

NVIDIA H200 Tensor Core GPU: 0 robots and 4 data centers 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 · NVIDIA H200 Tensor Core GPU full page · Tesla Dojo D1 full page.