DEPLOYDatabase

Chip comparison

AWS Trainium 2 vs NVIDIA B200 (Blackwell)

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 2NVIDIA B200 (Blackwell)
Process nodeTSMC N5TSMC 4NP
Transistors (B)208
TDP500 W1000 W
Memory96 GB HBM3192 GB HBM3e
Memory bandwidth2,900 GB/s8,000 GB/s
FP16 (dense)2,250 TFLOPS
BF16 (dense)667 TFLOPS2,250 TFLOPS
FP8 (dense)1,300 TFLOPS4,500 TFLOPS
INT8 (dense)1,300 TOPS4,500 TOPS
Form factorAWS Neuron 2 (Trn2 UltraServer with 64 chips per unit)SXM6
Announced2024-12-032024-03-18
Released2025-01-012024-11-01

Robots running AWS Trainium 2

No robots publicly running it yet.

Robots running NVIDIA B200 (Blackwell)

No robots publicly running it yet.

Common questions

AWS Trainium 2 vs NVIDIA B200 (Blackwell): which is faster for training?

NVIDIA B200 (Blackwell) has 3.46x the dense FP8 throughput of the other (AWS Trainium 2: 1,300 TFLOPS; NVIDIA B200 (Blackwell): 4,500 TFLOPS). Real-world training speed also depends on memory bandwidth, interconnect, and software stack: see the spec diff for those inputs.

AWS Trainium 2 vs NVIDIA B200 (Blackwell): which has more memory?

NVIDIA B200 (Blackwell) carries more HBM (AWS Trainium 2: 96 GB HBM3; NVIDIA B200 (Blackwell): 192 GB HBM3e). Memory capacity sets the largest model that fits on one chip without partitioning; memory bandwidth sets inference throughput.

AWS Trainium 2 vs NVIDIA B200 (Blackwell): which is more power-efficient?

Dense FP8 TFLOPS per watt: AWS Trainium 2 2.60 (1,300 ÷ 500 W); NVIDIA B200 (Blackwell) 4.50 (4,500 ÷ 1000 W). NVIDIA B200 (Blackwell) wins at the die level; system-level efficiency also depends on cooling and interconnect.

See also: every chip comparison · AWS Trainium 2 full page · NVIDIA B200 (Blackwell) full page.