Chip comparison
AWS Trainium (Trn1) vs Cerebras WSE-3 (Wafer-Scale Engine 3)
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.
| Field | AWS Trainium (Trn1) | Cerebras WSE-3 (Wafer-Scale Engine 3) |
|---|---|---|
| Process node | TSMC N7 | TSMC N5 |
| Transistors (B) | — | 4000 |
| Die size | — | 46225 mm² |
| TDP | 500 W | 23000 W |
| Memory | 32 GB HBM2e | 44 GB on-die SRAM |
| Memory bandwidth | 820 GB/s | 21,000,000 GB/s |
| FP16 (dense) | — | 62,500 TFLOPS |
| BF16 (dense) | 210 TFLOPS | — |
| Form factor | Trn1 EC2 instance | wafer-scale (single-wafer system) |
| Announced | 2020-12-01 | 2024-03-13 |
| Released | 2022-10-01 | 2024-04-01 |
Robots running AWS Trainium (Trn1)
No robots publicly running it yet.
Robots running Cerebras WSE-3 (Wafer-Scale Engine 3)
No robots publicly running it yet.
Data centers with AWS Trainium (Trn1)
No data centers publicly running it yet.
Data centers with Cerebras WSE-3 (Wafer-Scale Engine 3)
No data centers publicly running it yet.
Common questions
AWS Trainium (Trn1) vs Cerebras WSE-3 (Wafer-Scale Engine 3): which is faster for training?
Cerebras WSE-3 (Wafer-Scale Engine 3) has 297.62x the dense FP16/BF16 throughput of the other (AWS Trainium (Trn1): 210 TFLOPS; Cerebras WSE-3 (Wafer-Scale Engine 3): 62,500 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 Cerebras WSE-3 (Wafer-Scale Engine 3): which has more memory?
Cerebras WSE-3 (Wafer-Scale Engine 3) carries more HBM (AWS Trainium (Trn1): 32 GB HBM2e; Cerebras WSE-3 (Wafer-Scale Engine 3): 44 GB on-die SRAM). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.
AWS Trainium (Trn1) vs Cerebras WSE-3 (Wafer-Scale Engine 3): which is more power-efficient?
Dense FP16/BF16 TFLOPS per watt: AWS Trainium (Trn1) 0.42 (210 ÷ 500 W); Cerebras WSE-3 (Wafer-Scale Engine 3) 2.72 (62,500 ÷ 23000 W). Cerebras WSE-3 (Wafer-Scale Engine 3) wins at the die level; system-level efficiency also depends on cooling and interconnect.
See also: every chip comparison · AWS Trainium (Trn1) full page · Cerebras WSE-3 (Wafer-Scale Engine 3) full page.