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* PDH free memory skeleton

* Add PDH printing

* Add LUID support for Vulkan

* wire luid from ggml-vulkan to mem-dxgi-pdh file

* Fix to ggml-impl

* Continue skeleton

* Implemented ggml_dxgi_pdh_get_device_memory

* fix comments

* Fix - change value GB to bytes

* add ifdefs to only support windows and not linux

* modify error codes

* Finished ggml_dxgi_pdh_init() function

* completed ggml_dxgi_pdh_release()

* Formatting changes, add static to functions

* fix build errors

* fix go build error

* fix luid - now should match between dxgi and vulkan

* Fix the free memory reporting (was using copy by value, change to reference)

* keep only dxgi1_2.h

* Modifications based on PR feedback

* fix merge conflicts (2) and fix desc1.description printout

* move dxgi + pdh api calls to before the vendor specific library calls

* change from 3 samples to 1 sample for PDH

* modify when old_mode is set

* add fix for building MacOS

* fix release and returns for other vendors

* add patch file

* app: add code for macOS and Windows apps under 'app'

* app: add readme

* app: windows and linux only for now

* ci: fix ui CI validation

---------
Co-authored-by: jmorganca <jmorganca@gmail.com>

Also adjusts the vulkan windows build pattern to match recent changes in other backends
so incremental builds are faster.

The initial implementation of qwen3-vl:235b exceeded the maximum graph
size based on the number of tensors. Although this was later fixed
through the use of the mrope operation, we are close to the limit in
some cases. This updates to track the current llama.cpp usage of GGML.

We pass invalid pointers when we check the size of the required
compute graph before fitting. Some CUDA APIs validate these pointers
but we can just skip them during this phase. cudaMemsetAsync is one
of these that we weren't skipping but never took the code path that
used it. Now that we have enabled op_offload, we can hit it in
memory pressured situations.

In CPU only setups the LibOllamaPath was omitted causing
us not to load the ggml-cpu-XXX libraries during inference.

This will help bubble up more crash errors

This PR removes a redundant test from TestAPIEmbeddings
Contents of this test already exists in embed_test.go and model_arch_test.go

On Windows AMD IDs are numeric, and can reorder based on the filter environment.
By passing in the filter env on a full discovery refresh, we'll only look at the actual devices
and ignore unsupported iGPUs.  Without this, on some systems iGPU VRAM was incorrectly
being used to populate the dGPU.

When a model is partially offloaded to system RAM, we can either
do the calculations on the CPU or we can temporarily transfer the
data to the GPU to do the calculations there. Small batches tend
to be better on the CPU, large batches on the GPU.

The llamarunner used the GPU in most cases and the ollamarunner
used the CPU. Although the ollamarunner saw an improvement in
token generation performance, there was a large performance hit
in prompt processing (3-10x).

There is an existing heuristic to dynamically switch between these
two modes but in practice it doesn't have enough information to
accurately make that decision. This adds authoritative data to make
the check work to get the best of both worlds.

Fixes #12037

We currently allocate the worst case batch for max sized
batches, which corresponds to prompt processing. However,
there are some cases where the generated graph is different
for small and large batches. To ensure that we don't need
to allocate memory later after layout has taken place, we
should run the worst case batch both ways and take the larger
amount of memory.

This does not noticeably affect loading speed as the most expensive
part of this logic is from image processing and that does not
occur during token generation.

windows gets confused when we try to hand the stderr file descriptor to the subprocess children.  This ensures the log output
always shows up.

* ml(ggml): mrope
* interleave mrope

this change fixes two bugs with `ollama rm`:

1. before a model is removed, it will first be stopped. this only
   happens for the first argument and skipped for all other models
2. models are unloaded indiscriminately. this errors for cloud models
   and should be omitted

this change fixes images with an alpha channel by overlaying the image
onto a white background

* mulmat
* permute

Fixed broken docs links

Eats extra whitespace at the end/beginning of content

this should reduce zombies during integration runs

* Fix vulkan PCI ID and ID handling

Intel GPUs may not report PCI IDs which was leading to incorrect overlap
detection.  Switch to using the existing PCI IDs, however AMD GPUs claim not to
report PCI IDs, but actually do, so try anyway, as this is required for ADLX to
find the GPUs on Windows. Numeric IDs lead to scheduling problems, so this also
switches Vulkan to use UUID based IDs. The GPU discovery patches have been
squashed into a single patch to simplify future rebases.

* review comments

This reverts commit 5d347f6d.

This reverts commit 934dd9e1.