trainium

A Trn2 instance carries 16 Trainium2 chips with 1.5 TB of HBM and more dense bf16 FLOPS than an 8-GPU H100 node. Whether the cheaper FLOPS reach your workload depends on the Neuron compiler, the kernels you do not have yet, and an MFU number you must measure rather than assume.

The weights are portable and almost nothing else is. Four layers have to exist before the model runs at all, the quantization format is the one most likely to be missing, and the honest plan states what will not work in the first quarter rather than promising parity.

Decode is bandwidth-bound, so cost per token tracks dollars per terabyte per second, and on spec the MI300X wins by 2x. The table, the memory-capacity term that changes the replica shape, and the software-efficiency discount that decides whether the spec ranking survives a benchmark.