Chip comparison
Biren BR100 vs NVIDIA A10 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 | Biren BR100 | NVIDIA A10 Tensor Core GPU |
|---|---|---|
| Process node | TSMC N7 | Samsung 8N |
| Transistors (B) | 77 | 28.3 |
| Die size | — | 628 mm² |
| TDP | 550 W | 150 W |
| Memory | 64 GB HBM2e | 24 GB GDDR6 |
| Memory bandwidth | 2,300 GB/s | 600 GB/s |
| FP16 (dense) | 1,024 TFLOPS | 125 TFLOPS |
| BF16 (dense) | 1,024 TFLOPS | 125 TFLOPS |
| INT8 (dense) | 2,048 TOPS | 250 TOPS |
| Form factor | OAM | PCIe |
| Announced | 2022-08-09 | 2021-04-12 |
| Released | — | 2021-04-12 |
Robots running Biren BR100
No robots publicly running it yet.
Robots running NVIDIA A10 Tensor Core GPU
No robots publicly running it yet.
Data centers with Biren BR100
No data centers publicly running it yet.
Data centers with NVIDIA A10 Tensor Core GPU
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Common questions
Biren BR100 vs NVIDIA A10 Tensor Core GPU: which is faster for training?
Biren BR100 has 8.19x the dense FP16/BF16 throughput of the other (Biren BR100: 1,024 TFLOPS; NVIDIA A10 Tensor Core GPU: 125 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.
Biren BR100 vs NVIDIA A10 Tensor Core GPU: which has more memory?
Biren BR100 carries more HBM (Biren BR100: 64 GB HBM2e; NVIDIA A10 Tensor Core GPU: 24 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Biren BR100 vs NVIDIA A10 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Biren BR100 1.86 (1,024 ÷ 550 W); NVIDIA A10 Tensor Core GPU 0.83 (125 ÷ 150 W). Biren BR100 wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · Biren BR100 full page · NVIDIA A10 Tensor Core GPU full page.