DEPLOYDatabase

Chip comparison

AWS Trainium (Trn1) vs Huawei Ascend 910D

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 Trainium (Trn1)Huawei Ascend 910D
Process nodeTSMC N7SMIC N+2 (~7nm)
TDP500 W800 W
Memory32 GB HBM2e192 GB HBM3
Memory bandwidth820 GB/s4,000 GB/s
FP16 (dense)—1,200 TFLOPS
BF16 (dense)210 TFLOPS1,200 TFLOPS
FP8 (dense)—2,400 TFLOPS
Form factorTrn1 EC2 instanceOAM
Announced2020-12-012025-04-01
Released2022-10-012025-12-01

Robots running AWS Trainium (Trn1)

No robots publicly running it yet.

Robots running Huawei Ascend 910D

No robots publicly running it yet.

Data centers with AWS Trainium (Trn1)

No data centers publicly running it yet.

Data centers with Huawei Ascend 910D

No data centers publicly running it yet.

Common questions

AWS Trainium (Trn1) vs Huawei Ascend 910D: which is faster for training?

Huawei Ascend 910D has 11.43x the dense FP16/BF16 throughput of the other (AWS Trainium (Trn1): 210 TFLOPS; Huawei Ascend 910D: 2,400 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

AWS Trainium (Trn1) vs Huawei Ascend 910D: which has more memory?

Huawei Ascend 910D carries more HBM (AWS Trainium (Trn1): 32 GB HBM2e; Huawei Ascend 910D: 192 GB HBM3). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

AWS Trainium (Trn1) vs Huawei Ascend 910D: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: AWS Trainium (Trn1) 0.42 (210 ÷ 500 W); Huawei Ascend 910D 3.00 (2,400 ÷ 800 W). Huawei Ascend 910D wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · AWS Trainium (Trn1) full page · Huawei Ascend 910D full page.