Chip comparison
Biren BR100 vs NVIDIA T4 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 T4 Tensor Core GPU |
|---|---|---|
| Process node | TSMC N7 | TSMC 12FFN |
| Transistors (B) | 77 | 13.6 |
| Die size | — | 545 mm² |
| TDP | 550 W | 70 W |
| Memory | 64 GB HBM2e | 16 GB GDDR6 |
| Memory bandwidth | 2,300 GB/s | 320 GB/s |
| FP16 (dense) | 1,024 TFLOPS | 65 TFLOPS |
| BF16 (dense) | 1,024 TFLOPS | — |
| INT8 (dense) | 2,048 TOPS | 130 TOPS |
| Form factor | OAM | PCIe (single-slot, low-profile) |
| Announced | 2022-08-09 | 2018-09-13 |
| Released | — | 2019-01-01 |
Robots running Biren BR100
No robots publicly running it yet.
Robots running NVIDIA T4 Tensor Core GPU
No robots publicly running it yet.
Data centers with Biren BR100
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Data centers with NVIDIA T4 Tensor Core GPU
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Common questions
Biren BR100 vs NVIDIA T4 Tensor Core GPU: which is faster for training?
Biren BR100 has 15.75x the dense FP16/BF16 throughput of the other (Biren BR100: 1,024 TFLOPS; NVIDIA T4 Tensor Core GPU: 65 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 T4 Tensor Core GPU: which has more memory?
Biren BR100 carries more HBM (Biren BR100: 64 GB HBM2e; NVIDIA T4 Tensor Core GPU: 16 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
Biren BR100 vs NVIDIA T4 Tensor Core GPU: which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: Biren BR100 1.86 (1,024 ÷ 550 W); NVIDIA T4 Tensor Core GPU 0.93 (65 ÷ 70 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 T4 Tensor Core GPU full page.