AWS Inferentia2 vs AWS Trainium
The Inferentia2 and the Trainium deliver near-identical FP8 throughput (190 vs 190 TFLOPS dense).
AWS Inferentia2
Full specs →AWS Trainium
Full specs →Specifications
Performance (TFLOPS)
FLOPS by Precision
What actually differs
The Inferentia2 is the newer part: NeuronCore-v2, launched in 2022, against the Trainium's NeuronCore-v2 from 2020. Newer architectures typically add lower-precision formats and better throughput per watt, so check the precision rows your workload actually uses.
Dense throughput for the Inferentia2 against the Trainium: FP32 48 vs 48 TFLOPS, FP16 190 vs 190 TFLOPS, FP8 190 vs 190 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.
Memory is 32 GB against 32 GB, fed at 820 GB/s versus 820 GB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.
For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.
Frequently asked questions
Is the Inferentia2 faster than the Trainium?
They are close on paper: both deliver about 190 TFLOPS of dense FP8 throughput. Memory, bandwidth, and power are the deciding differences.
Which has more memory, the Inferentia2 or the Trainium?
Both GPUs carry 32 GB of memory. Memory bandwidth is 820 GB/s for the Inferentia2 and 820 GB/s for the Trainium.
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