Model card synced with fastforks 7204e33f

README.md

@@ -63,6 +63,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `igev-plusplus-eth3d` not meas
 | --- | --- | --- | --- | --- | --- | --- |
 | `igev-plusplus-eth3d` | 0.42 (375x1242) | 0.20 | 0.16† | 2.33 (375x1242) | 1.10† | 0.40† |
 
+### torch.compile and TensorRT
+
+| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| `igev-plusplus-eth3d` | 994x1482 | 1080.64 | 715.6 | 159.0 | 494.4 | 425 | 393.04 | 1594 / 1840 | 952 / 1074 | 3632 |
+
 ### Fixed-size speed and peak memory, all models
 
 | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
@@ -78,6 +84,12 @@ Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `igev-plusplus-eth3d` not meas
 | `igev-plusplus-sceneflow` | portable | 201.0 ms / 354 / 612 | 606.2 ms / 987 / 1662 | 1534.9 ms / 2172 / 3496 | OOM |
 | `igev-plusplus-sceneflow` | kernels | 88.6 ms / 357 / 1092 | 240.6 ms / 830 / 1210 | 535.4 ms / 1799 / 2464 | 3143.9 ms (n=3) / 8419 / 9752 |
 
+### Apple silicon (MPS)
+
+| model | `portable` | `kernels` | speed-up |
+| --- | --- | --- | --- |
+| `igev-plusplus-sceneflow` | 2852 ms / 1783 MiB (float16) | 2054 ms / 1803 MiB | 1.39x |
+
 Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
 
 - `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`: same network and settings as `igev-plusplus-eth3d`, measured once (rows not repeated).
@@ -99,7 +111,7 @@ Every variant is checked against golden outputs of the original code at its pinn
 ```python
 from fastforks import stereo
 
-model = stereo.load("igev-plusplus-eth3d")  # portable PyTorch on the GPU if there is one, else the CPU
+model = stereo.load("igev-plusplus-eth3d")  # mode="auto": the preferred backend from recorded measurements
 result = model("left.png", "right.png")
 result.disparity  # [H, W] float32 tensor, input resolution
 result.backend  # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
@@ -110,3 +122,5 @@ Install with `pip install fastforks` (see <https://github.com/kleinicke/fastfork
 ## Kernels backend
 
 `stereo.load("igev-plusplus-eth3d", mode="kernels")` runs our CUDA kernels (`igev`, `igevpp`, `cutlass_conv`, `cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from <https://huggingface.co/fastforks/kernels> on first use) and a recent NVIDIA driver; if there is none it raises `BackendUnavailableError` with the reason and never falls back silently. `mode="auto"` picks `kernels` when it can run and logs the choice.
+
+<!-- staged from fastforks 7204e33f (2026-11-05) -->