How to correctly install JAX with CUDA on Linux when `jax[cuda12_pip]` consistently falls back to the CPU version?

I am trying to install JAX with GPU support on a powerful, dedicated Linux server, but I am stuck in what feels like a Catch-22 where every official installation method fails in a different way, always resulting in JAX falling back to the CPU.

I am looking for a definitive, foolproof set of commands to get a working GPU installation.

System Specifications:

• OS: Ubuntu 18.04 LTS
• GPU: 8x NVIDIA Quadro RTX 8000
• NVIDIA Driver: 550.144.03
• CUDA Version (reported by driver): 12.4
• Python: 3.10 (managed by Conda)

What I Have Tried

I have meticulously created fresh conda environments for each attempt to ensure there are no conflicts.

Attempt #1: The Standard Recommended Method

This is the official recommended command.

conda create -n jax_test python=3.10 -y
conda activate jax_test
pip install "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html --no-cache-dir

• Expected Result: A large, multi-gigabyte jaxlib wheel with CUDA libraries included should be downloaded and installed.
• Actual Result: pip consistently ignores the [cuda12_pip] directive, downloads the small CPU version of jaxlib (89.9 MB), and gives a warning. The verification command confirms this failure:

WARNING: jax 0.6.2 does not provide the extra 'cuda12-pip'
Downloading jaxlib-0.6.2-cp310-cp310-manylinux2014_x86_64.whl (89.9 MB)
...
$ python -c "import jax; print(jax.devices())"
WARNING: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
[CpuDevice(id=0)]

Attempt #2: The Direct URL Method

This is the expert workaround to force installation of a specific GPU wheel.

# In a clean environment...
pip install "https://storage.googleapis.com/jax-releases/cuda12/jaxlib-0.4.23+cuda12.cudnn88-cp310-cp310-manylinux2014_x86_64.whl"
pip install jax==0.4.23 "numpy<2.0"

• Expected Result: The specific, all-in-one GPU jaxlib is installed.
• Actual Result: The installation fails because the URL is dead. Google has apparently removed older files from their storage.

ERROR: HTTP error 404 while getting https://.../jaxlib-0.4.23...whl

This shows that relying on specific, old URLs is not a stable solution.

Attempt #3: The Staged Plugin Method

This involves installing the CUDA plugin first, then JAX.

# In a clean environment...
pip install --upgrade "jax-cuda12-plugin"
pip install jax

• Expected Result: JAX should install and then use the jaxlib provided by the plugin.
• Actual Result: This leads to a dependency conflict. Installing jax re-installs the wrong CPU version of jaxlib over the plugin's libraries, leading back to the same "fallback to CPU" problem as Attempt #1.

My Question

I am completely stuck.

• The standard installer fails to select the GPU package.
• Direct URL installation fails because the files are gone.
• The plugin-based method creates a dependency conflict that breaks the installation.

Given my server specifications (Ubuntu 18.04, CUDA 12.4 compatible driver), what is the current, definitive, and guaranteed-to-work set of commands to install a version of JAX that successfully uses the GPU?