Clarification on strict prerequisites (Python 3.12+, PyTorch 2.7+) vs. backward compatibility

I noticed that the README lists the following strict prerequisites:

> Python 3.12 or higher
>
> PyTorch 2.7 or higher
>
> CUDA-compatible GPU with CUDA 12.6 or higher

However, I have successfully installed and run the model in a slightly older environment:

- Python: 3.10
-
- PyTorch: 2.6.0+cu124
-
- OS: Linux

I was able to run basic inference and several simple examples without errors.

My Question Could you clarify which specific components or features of SAM 3 necessitate Python 3.12+ and PyTorch 2.7+?

I am asking because many standard production and enterprise environments (e.g., cloud generic runtimes) are not yet updated to Python 3.12 or CUDA 12.6. I would like to know:

Is the requirement strict due to specific language features (e.g., new typing/async features in 3.12) or specific operator optimizations in PyTorch 2.7?

Are there specific modules (e.g., video tracking, compilation, or specific decoders) that are known to fail on Python 3.10/3.11?

Is it safe to rely on a Python 3.10 build for inference, or are there silent failures/performance degradations expected?

Start with the README prerequisites and reproduce the reported Python 3.10, PyTorch 2.6.0+cu124 Linux setup using the basic inference and simple examples mentioned in the issue. Done means documenting which requirements are strict, which components or features depend on newer versions, and whether the older environment is supported.

Written by the indexing model from the issue text.