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Chip comparison

AWS Inferentia 2 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.

FieldAWS Inferentia 2NVIDIA A10 Tensor Core GPU
Process nodeTSMC N7Samsung 8N
Transistors (B)28.3
Die size628 mm²
TDP250 W150 W
Memory32 GB HBM324 GB GDDR6
Memory bandwidth820 GB/s600 GB/s
FP16 (dense)125 TFLOPS
BF16 (dense)190 TFLOPS125 TFLOPS
FP8 (dense)380 TFLOPS
INT8 (dense)250 TOPS
Form factorInf2 EC2 instancePCIe
Announced2022-12-012021-04-12
Released2023-04-012021-04-12

Robots running AWS Inferentia 2

No robots publicly running it yet.

Robots running NVIDIA A10 Tensor Core GPU

No robots publicly running it yet.

Data centers with AWS Inferentia 2

No data centers publicly running it yet.

Data centers with NVIDIA A10 Tensor Core GPU

No data centers publicly running it yet.

Common questions

AWS Inferentia 2 vs NVIDIA A10 Tensor Core GPU: which is faster for training?

AWS Inferentia 2 has 3.04x the dense FP16/BF16 throughput of the other (AWS Inferentia 2: 380 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.

AWS Inferentia 2 vs NVIDIA A10 Tensor Core GPU: which has more memory?

AWS Inferentia 2 carries more HBM (AWS Inferentia 2: 32 GB HBM3; 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.

AWS Inferentia 2 vs NVIDIA A10 Tensor Core GPU: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: AWS Inferentia 2 1.52 (380 ÷ 250 W); NVIDIA A10 Tensor Core GPU 0.83 (125 ÷ 150 W). AWS Inferentia 2 wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · AWS Inferentia 2 full page · NVIDIA A10 Tensor Core GPU full page.