LLM benchmark methodology

Emulating bfloat16 on a T4G burns 87% of decode on dtype conversion

Identical code and identical weights diverge 3.7x on decode between a T4G and an L4, because Turing has no bf16 datapath and XLA emulates one in fp32 while the instance keeps returning 200s and green health checks.