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Chip comparison

Biren BR100 vs Google TPU v7 (Ironwood)

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.

FieldBiren BR100Google TPU v7 (Ironwood)
Process nodeTSMC N7
Transistors (B)77
TDP550 W700 W
Memory64 GB HBM2e192 GB HBM3e
Memory bandwidth2,300 GB/s7,400 GB/s
FP16 (dense)1,024 TFLOPS
BF16 (dense)1,024 TFLOPS4,614 TFLOPS
FP8 (dense)9,228 TFLOPS
INT8 (dense)2,048 TOPS
Form factorOAMOAM (Ironwood pod, up to 9,216 chips)
Announced2022-08-092025-04-09
Released2025-12-01

Robots running Biren BR100

No robots publicly running it yet.

Robots running Google TPU v7 (Ironwood)

No robots publicly running it yet.

Data centers with Biren BR100

No data centers publicly running it yet.

Data centers with Google TPU v7 (Ironwood)

No data centers publicly running it yet.

Common questions

Biren BR100 vs Google TPU v7 (Ironwood): which is faster for training?

Google TPU v7 (Ironwood) has 9.01x the dense FP16/BF16 throughput of the other (Biren BR100: 1,024 TFLOPS; Google TPU v7 (Ironwood): 9,228 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

Biren BR100 vs Google TPU v7 (Ironwood): which has more memory?

Google TPU v7 (Ironwood) carries more HBM (Biren BR100: 64 GB HBM2e; Google TPU v7 (Ironwood): 192 GB HBM3e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

Biren BR100 vs Google TPU v7 (Ironwood): which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: Biren BR100 1.86 (1,024 ÷ 550 W); Google TPU v7 (Ironwood) 13.18 (9,228 ÷ 700 W). Google TPU v7 (Ironwood) wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · Biren BR100 full page · Google TPU v7 (Ironwood) full page.