DEPLOYDatabase

Chip comparison

AWS Inferentia 2 vs Huawei Ascend 910C

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.

FieldAWS Inferentia 2Huawei Ascend 910C
Process nodeTSMC N7SMIC N+2 (~7nm)
TDP250 W500 W
Memory32 GB HBM3128 GB HBM2e
Memory bandwidth820 GB/s3,200 GB/s
FP16 (dense)—800 TFLOPS
BF16 (dense)190 TFLOPS800 TFLOPS
FP8 (dense)380 TFLOPS—
INT8 (dense)—1,600 TOPS
Form factorInf2 EC2 instanceOAM
Announced2022-12-012024-09-19
Released2023-04-012025-01-01

Robots running AWS Inferentia 2

No robots publicly running it yet.

Robots running Huawei Ascend 910C

No robots publicly running it yet.

Data centers with AWS Inferentia 2

No data centers publicly running it yet.

Data centers with Huawei Ascend 910C

No data centers publicly running it yet.

Common questions

AWS Inferentia 2 vs Huawei Ascend 910C: which is faster for training?

Huawei Ascend 910C has 2.11x the dense FP16/BF16 throughput of the other (AWS Inferentia 2: 380 TFLOPS; Huawei Ascend 910C: 800 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

AWS Inferentia 2 vs Huawei Ascend 910C: which has more memory?

Huawei Ascend 910C carries more HBM (AWS Inferentia 2: 32 GB HBM3; Huawei Ascend 910C: 128 GB HBM2e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

AWS Inferentia 2 vs Huawei Ascend 910C: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: AWS Inferentia 2 1.52 (380 ÷ 250 W); Huawei Ascend 910C 1.60 (800 ÷ 500 W). Huawei Ascend 910C wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · AWS Inferentia 2 full page · Huawei Ascend 910C full page.