NvidiaBackend.__init__ leaves self.device None. The loader picked the precision
default as `'fp16' if self.device == 'cuda' else 'fp32'` at line 897 and only
detected the device at line 1029, so the FIRST load in a fresh engine process
defaulted to fp32 on a CUDA card -- while a second load in the same process
inherited 'cuda' from the first and quietly got fp16. The same model loaded at
two precisions depending on load order.

fp32 costs twice the VRAM of the dtype asked for, which on a 24 GB card is the
whole headroom argument for quantizing in the first place: Qwen3.5-9B is
carded load_in_4bit with an estimate of 7.795 GB and measured 14.403 GB, a
1.85x gap that is the fp32 tell. FlashAttention also refuses fp32 outright, so
transformers warned twice at load, cast the inputs back to fp32 at inference,
and the model answered every request with "FlashAttention only support fp16 and
bf16 data type" -- and before that error was surfaced at all, with an empty
completion. It had been unusable since 2026-09-30.

Also resolve flash_attn by PRESENCE rather than truthiness: `config.get(k) or
global` cannot express "off", so a card saying `flash_attention: false` fell
through to the global default. Not the cause here (the global is false too),
but the same class of bug one layer up.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014T3rZnLhydX9QqUcfBbqLu