How to enable CUDA Minor Version Compatibility for/in a nvidia/cuda docker image

Accelerated Computing NGC GPU Cloud Container: CUDA
1

I want to run this nvidia/cuda:12.8 image on a cuda 12.4 machine (rtx 3060).

docker run -it --gpus all --rm nvidia/cuda:12.8.1-runtime-ubuntu24.04 

docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running prestart hook #0: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'
nvidia-container-cli: requirement error: unsatisfied condition: cuda>=12.8, please update your driver to a newer version, or use an earlier cuda container: unknown

I notice that nvidia/pytorch:25.02-py3 has this CUDA Minor Version Compatibility mode ENABLED.

docker run -it --gpus all --rm nvcr.io/nvidia/pytorch:25.02-py3


=============
== PyTorch ==
=============

NVIDIA Release 25.02 (build 143088496)
PyTorch Version 2.7.0a0+ecf3bae
Container image Copyright (c) 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Copyright (c) 2014-2024 Facebook Inc.
Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
Copyright (c) 2011-2013 NYU                      (Clement Farabet)
Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
Copyright (c) 2015      Google Inc.
Copyright (c) 2015      Yangqing Jia
Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

WARNING: CUDA Minor Version Compatibility mode ENABLED.
  Using driver version 550.120 which has support for CUDA 12.4.  This container
  was built with CUDA 12.8 and will be run in Minor Version Compatibility mode.
  CUDA Forward Compatibility is preferred over Minor Version Compatibility for use
  with this container but was unavailable:
  [[Forward compatibility was attempted on non supported HW (CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE) cuInit()=804]]
  See https://docs.nvidia.com/deploy/cuda-compatibility/ for details.

NOTE: The SHMEM allocation limit is set to the default of 64MB.  This may be
   insufficient for PyTorch.  NVIDIA recommends the use of the following flags:
   docker run --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ...

How do I build an image based on nvidia/cuda:12.8.1 with that kind of compability?