DEPLOYDatabase

Chip comparison

NVIDIA GB300 (Blackwell Ultra) 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 GB300 (Blackwell Ultra)Tesla Dojo D1
Process nodeTSMC 4NPTSMC N7
Transistors (B)50
Die size645 mm²
TDP1400 W400 W
Memory288 GB HBM3e
Memory bandwidth8,000 GB/s
BF16 (dense)362 TFLOPS
FP8 (dense)5,000 TFLOPS362 TFLOPS
Form factorSuperchip (Blackwell Ultra + Grace)Training tile (25 D1 chips per tile)
Announced2025-03-182021-08-19
Released2025-11-012023-07-01

Robots running NVIDIA GB300 (Blackwell Ultra)

No robots publicly running it yet.

Robots running Tesla Dojo D1

No robots publicly running it yet.

Data centers with NVIDIA GB300 (Blackwell Ultra)

Data centers with Tesla Dojo D1

No data centers publicly running it yet.

Common questions

NVIDIA GB300 (Blackwell Ultra) vs Tesla Dojo D1: which is faster for training?

NVIDIA GB300 (Blackwell Ultra) has 13.81x the dense FP8 throughput of the other (NVIDIA GB300 (Blackwell Ultra): 5,000 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 GB300 (Blackwell Ultra) vs Tesla Dojo D1: which is more power-efficient?

Dense FP8 TFLOPS per watt: NVIDIA GB300 (Blackwell Ultra) 3.57 (5,000 ÷ 1400 W); Tesla Dojo D1 0.91 (362 ÷ 400 W). NVIDIA GB300 (Blackwell Ultra) wins at the die level; system-level efficiency also depends on cooling and interconnect.

NVIDIA GB300 (Blackwell Ultra) vs Tesla Dojo D1: which is more widely used?

NVIDIA GB300 (Blackwell Ultra): 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 · NVIDIA GB300 (Blackwell Ultra) full page · Tesla Dojo D1 full page.