The previous commit budgets any Vulkan/HIP device that `llama-server
--list-devices` prints as a discrete card. That output carries no
integrated-vs-discrete verdict, so an iGPU (Intel ADL-N reports 11.8 GB of
a 16 GB machine's shared memory) would be counted twice: once as VRAM and
again as system-RAM spill.
Ask the installed engine's ggml library instead, in a bounded child
process: ggml_backend_dev_type() is GPU for a discrete card and IGPU for
an integrated one (Vulkan reads it from VkPhysicalDeviceType). llama.cpp
places layers on discrete devices whenever one exists and falls back to an
integrated GPU only otherwise, so the budget follows the same rule:
- discrete device -> its own memory, system RAM as spill (uma=False)
- integrated GPU, no answer, CPU/Metal -> RAM-as-unified, unchanged
The probe runs only when no NVIDIA device answered and the configured
engine is Vulkan/HIP, and is cached per engine binary (the hardware
endpoint polls every few seconds). The CUDA --list-devices fallback for the
NVIDIA unified-pool quirk is left as it was on main.
Co-authored-by: Belaquaa <eee_pokkk@mail.ru>
2e6924bd10