Chip comparison
NVIDIA GB300 (Blackwell Ultra) vs NVIDIA H100 Tensor Core GPU
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 GB300 (Blackwell Ultra) | NVIDIA H100 Tensor Core GPU |
|---|---|---|
| Process node | TSMC 4NP | TSMC 4N |
| Transistors (B) | — | 80 |
| Die size | — | 814 mm² |
| TDP | 1400 W | 700 W |
| Memory | 288 GB HBM3e | 80 GB HBM3 |
| Memory bandwidth | 8,000 GB/s | 3,350 GB/s |
| FP16 (dense) | — | 989 TFLOPS |
| BF16 (dense) | — | 989 TFLOPS |
| FP8 (dense) | 5,000 TFLOPS | 1,979 TFLOPS |
| INT8 (dense) | — | 1,979 TOPS |
| Launch price (list) | — | $30,000 |
| Form factor | Superchip (Blackwell Ultra + Grace) | SXM5 |
| Announced | 2025-03-18 | 2022-03-22 |
| Released | 2025-11-01 | 2022-10-13 |
Robots running NVIDIA GB300 (Blackwell Ultra)
No robots publicly running it yet.
Robots running NVIDIA H100 Tensor Core GPU
No robots publicly running it yet.
Data centers with NVIDIA GB300 (Blackwell Ultra)
Common questions
NVIDIA GB300 (Blackwell Ultra) vs NVIDIA H100 Tensor Core GPU: which is faster for training?
NVIDIA GB300 (Blackwell Ultra) has 2.53x the dense FP8 throughput of the other (NVIDIA GB300 (Blackwell Ultra): 5,000 TFLOPS; NVIDIA H100 Tensor Core GPU: 1,979 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 NVIDIA H100 Tensor Core GPU: which has more memory?
NVIDIA GB300 (Blackwell Ultra) carries more HBM (NVIDIA GB300 (Blackwell Ultra): 288 GB HBM3e; NVIDIA H100 Tensor Core GPU: 80 GB HBM3). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
NVIDIA GB300 (Blackwell Ultra) vs NVIDIA H100 Tensor Core GPU: which is more power-efficient?
Dense FP8 TFLOPS per watt: NVIDIA GB300 (Blackwell Ultra) 3.57 (5,000 ÷ 1400 W); NVIDIA H100 Tensor Core GPU 2.83 (1,979 ÷ 700 W). NVIDIA GB300 (Blackwell Ultra) wins at the die level; system-level efficiency also depends on cooling and interconnect.
NVIDIA GB300 (Blackwell Ultra) vs NVIDIA H100 Tensor Core GPU: which is more widely used?
NVIDIA GB300 (Blackwell Ultra): 0 robots and 1 data center publicly running it. NVIDIA H100 Tensor Core GPU: 0 robots and 7 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 · NVIDIA H100 Tensor Core GPU full page.