Chip comparison
NVIDIA H100 Tensor Core GPU vs Tesla HW4 (FSD Computer 2)
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 H100 Tensor Core GPU | Tesla HW4 (FSD Computer 2) |
|---|---|---|
| Process node | TSMC 4N | Samsung 7LPP |
| Transistors (B) | 80 | — |
| Die size | 814 mm² | — |
| TDP | 700 W | 90 W |
| Memory | 80 GB HBM3 | 16 GB GDDR6 |
| Memory bandwidth | 3,350 GB/s | — |
| FP16 (dense) | 989 TFLOPS | — |
| BF16 (dense) | 989 TFLOPS | — |
| FP8 (dense) | 1,979 TFLOPS | — |
| INT8 (dense) | 1,979 TOPS | 300 TOPS |
| Launch price (list) | $30,000 | — |
| Form factor | SXM5 | Auto-board (dual-SoC) |
| Announced | 2022-03-22 | 2023-01-01 |
| Released | 2022-10-13 | 2023-02-01 |
Robots running NVIDIA H100 Tensor Core GPU
No robots publicly running it yet.
Robots running Tesla HW4 (FSD Computer 2)
Data centers with NVIDIA H100 Tensor Core GPU
Data centers with Tesla HW4 (FSD Computer 2)
No data centers publicly running it yet.
Common questions
NVIDIA H100 Tensor Core GPU vs Tesla HW4 (FSD Computer 2): which has more memory?
NVIDIA H100 Tensor Core GPU carries more HBM (NVIDIA H100 Tensor Core GPU: 80 GB HBM3; Tesla HW4 (FSD Computer 2): 16 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
NVIDIA H100 Tensor Core GPU vs Tesla HW4 (FSD Computer 2): which is more widely used?
NVIDIA H100 Tensor Core GPU: 0 robots and 7 data centers publicly running it. Tesla HW4 (FSD Computer 2): 3 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 H100 Tensor Core GPU full page · Tesla HW4 (FSD Computer 2) full page.