The last reason MM-Embed ran on the CPU, and the one that only the new
diagnosis could have found. Its quantized GPU load died in accelerate's own
frame:

    transformers/modeling_utils.py:3904 in from_pretrained
      dispatch_model(model, **device_map_kwargs)
    accelerate/big_modeling.py:514 in dispatch_model
      model.to(device)
    transformers/modeling_utils.py:2774 in to
      ValueError: `.to` is not supported for `4-bit` or `8-bit` bitsandbytes
      models.

accelerate 1.x takes a `model.to(device)` shortcut for a single-device map, and
transformers 4.42 refuses that for a bnb model -- so the fallback ran it on the
CPU at 30x the latency and 33 GB of host RAM, for a model that fits in 5.7 GB
of card. accelerate==0.32.1 now sits in that pin's extras with peft, datasets,
pyarrow and bitsandbytes; the shared requirements carry none of them, because
only the quantizing pin needs them.

Verified live on zeiss after the relaunch:

    [tf4-embed] nvidia/MM-Embed ready on cuda (4096-d, transformers 4.42.4,
                quantized)
    card: 5666 MiB   (the planner estimated 5.18 GB)
    text: dims=4096 finite=True   image: dims=4096 finite=True
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014T3rZozi1Ehk96NG1WesgC