Document immutable installation and CUDA TF32 usage

README.md

@@ -145,3 +145,52 @@ An explicit CPU comparison is available through `inference[torch]` and
 `backend="pytorch"`. The package verifies payload digests before inference.
 
 Runtime reference: https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html
+
+## Quick start — CUDA + TF32
+
+Requires supported Linux x86-64/NVIDIA CUDA setup and Python 3.12 or 3.13.
+Install the download helper, then the immutable inference source:
+
+```bash
+python -m pip install "huggingface-hub>=0.36,<2"
+```
+
+```python
+import subprocess
+import sys
+from huggingface_hub import snapshot_download
+
+source = snapshot_download(
+    "AutomatosX/AXDocLayout-M-v1.2",
+    revision="1b4ed073124fddb0c8dce4f915b7b22aa9fab8ee",
+    allow_patterns=["inference/**"],
+    token=False,
+)
+subprocess.check_call([
+    sys.executable, "-m", "pip", "install", source + "/inference[onnx]"
+])
+```
+
+Run CUDA-powered layout detection:
+
+```python
+import numpy as np
+from PIL import Image
+from ax_doclayout import AXDocLayout, PageImage
+
+model = AXDocLayout.from_pretrained(
+    "AutomatosX/AXDocLayout-M-v1.2",
+    revision="7efd000fc7a1ef50ed7913835f2d8f10f30dc8e3",
+    backend="onnx",  # v1.2: CUDAExecutionProvider with TF32 enabled
+)
+page = PageImage(
+    rgb=np.asarray(Image.open("page.png").convert("RGB"), dtype=np.uint8),
+    page_index=0,
+)
+result = model.predict(page)
+print(result.to_json())
+```
+
+For the separate CPU PyTorch comparison, install `inference[torch]` and
+explicitly select `backend="pytorch"`. The CUDA ONNX loader does not switch
+to this backend automatically.