Chip comparison
NVIDIA A10 Tensor Core GPU vs NVIDIA GB300 (Blackwell Ultra)
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 A10 Tensor Core GPU | NVIDIA GB300 (Blackwell Ultra) |
|---|---|---|
| Process node | Samsung 8N | TSMC 4NP |
| Transistors (B) | 28.3 | — |
| Die size | 628 mm² | — |
| TDP | 150 W | 1400 W |
| Memory | 24 GB GDDR6 | 288 GB HBM3e |
| Memory bandwidth | 600 GB/s | 8,000 GB/s |
| FP16 (dense) | 125 TFLOPS | — |
| BF16 (dense) | 125 TFLOPS | — |
| FP8 (dense) | — | 5,000 TFLOPS |
| INT8 (dense) | 250 TOPS | — |
| Form factor | PCIe | Superchip (Blackwell Ultra + Grace) |
| Announced | 2021-04-12 | 2025-03-18 |
| Released | 2021-04-12 | 2025-11-01 |
Robots running NVIDIA A10 Tensor Core GPU
No robots publicly running it yet.
Robots running NVIDIA GB300 (Blackwell Ultra)
No robots publicly running it yet.
Data centers with NVIDIA A10 Tensor Core GPU
No data centers publicly running it yet.
Data centers with NVIDIA GB300 (Blackwell Ultra)
Common questions
NVIDIA A10 Tensor Core GPU vs NVIDIA GB300 (Blackwell Ultra): which is faster for training?
NVIDIA GB300 (Blackwell Ultra) has 40.00x the dense FP16/BF16 throughput of the other (NVIDIA A10 Tensor Core GPU: 125 TFLOPS; NVIDIA GB300 (Blackwell Ultra): 5,000 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
NVIDIA A10 Tensor Core GPU vs NVIDIA GB300 (Blackwell Ultra): which has more memory?
NVIDIA GB300 (Blackwell Ultra) carries more HBM (NVIDIA A10 Tensor Core GPU: 24 GB GDDR6; NVIDIA GB300 (Blackwell Ultra): 288 GB HBM3e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
NVIDIA A10 Tensor Core GPU vs NVIDIA GB300 (Blackwell Ultra): which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: NVIDIA A10 Tensor Core GPU 0.83 (125 ÷ 150 W); NVIDIA GB300 (Blackwell Ultra) 3.57 (5,000 ÷ 1400 W). NVIDIA GB300 (Blackwell Ultra) wins at the die level; system-level efficiency also depends on cooling and interconnect.
NVIDIA A10 Tensor Core GPU vs NVIDIA GB300 (Blackwell Ultra): which is more widely used?
NVIDIA A10 Tensor Core GPU: 0 robots and 0 data centers publicly running it. NVIDIA GB300 (Blackwell Ultra): 0 robots and 1 data center. Empty here means no operator has said publicly, not zero adoption in the market.
See also: every chip comparison · NVIDIA A10 Tensor Core GPU full page · NVIDIA GB300 (Blackwell Ultra) full page.