DEPLOYDatabase

Chip comparison

AWS Trainium (Trn1) vs Tenstorrent Blackhole

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)Tenstorrent Blackhole
Process nodeTSMC N7TSMC N6
TDP500 W300 W
Memory32 GB HBM2e32 GB GDDR6
Memory bandwidth820 GB/s512 GB/s
FP16 (dense)745 TFLOPS
BF16 (dense)210 TFLOPS
INT8 (dense)2,980 TOPS
Form factorTrn1 EC2 instancePCIe
Announced2020-12-012024-07-01
Released2022-10-012025-01-01

Robots running AWS Trainium (Trn1)

No robots publicly running it yet.

Robots running Tenstorrent Blackhole

No robots publicly running it yet.

Data centers with AWS Trainium (Trn1)

No data centers publicly running it yet.

Data centers with Tenstorrent Blackhole

No data centers publicly running it yet.

Common questions

AWS Trainium (Trn1) vs Tenstorrent Blackhole: which is faster for training?

Tenstorrent Blackhole has 3.55x the dense FP16/BF16 throughput of the other (AWS Trainium (Trn1): 210 TFLOPS; Tenstorrent Blackhole: 745 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 Tenstorrent Blackhole: which has more memory?

Tenstorrent Blackhole carries more HBM (AWS Trainium (Trn1): 32 GB HBM2e; Tenstorrent Blackhole: 32 GB GDDR6). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

AWS Trainium (Trn1) vs Tenstorrent Blackhole: which is more power-efficient?

Dense FP16/BF16 TFLOPS per watt: AWS Trainium (Trn1) 0.42 (210 ÷ 500 W); Tenstorrent Blackhole 2.48 (745 ÷ 300 W). Tenstorrent Blackhole wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · AWS Trainium (Trn1) full page · Tenstorrent Blackhole full page.