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Chip comparison

NVIDIA GB200 Grace Blackwell Superchip 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 GB200 Grace Blackwell SuperchipTesla Dojo D1
Process nodeTSMC 4NPTSMC N7
Transistors (B)50
Die size645 mm²
TDP2700 W400 W
Memory384 GB HBM3e
Memory bandwidth16,000 GB/s
BF16 (dense)362 TFLOPS
FP8 (dense)9,000 TFLOPS362 TFLOPS
Form factorSuperchip (2xB200 + 1xGrace)Training tile (25 D1 chips per tile)
Announced2024-03-182021-08-19
Released2024-12-012023-07-01

Robots running NVIDIA GB200 Grace Blackwell Superchip

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 GB200 Grace Blackwell Superchip vs Tesla Dojo D1: which is faster for training?

NVIDIA GB200 Grace Blackwell Superchip has 24.86x the dense FP8 throughput of the other (NVIDIA GB200 Grace Blackwell Superchip: 9,000 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 GB200 Grace Blackwell Superchip vs Tesla Dojo D1: which is more power-efficient?

Dense FP8 TFLOPS per watt: NVIDIA GB200 Grace Blackwell Superchip 3.33 (9,000 ÷ 2700 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). NVIDIA GB200 Grace Blackwell Superchip wins at the die level; system-level efficiency also depends on cooling and interconnect.

NVIDIA GB200 Grace Blackwell Superchip vs Tesla Dojo D1: which is more widely used?

NVIDIA GB200 Grace Blackwell Superchip: 0 robots and 11 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 GB200 Grace Blackwell Superchip full page · Tesla Dojo D1 full page.