If you are setting `page_pool_size` lower than the `pages_limit` you may want to try increasing, eg `amdgpu.vm_fragment_size=8` (4=64K default, 9=2M) to allocate in bigger chunks.
### ROCm
-
The latest release version of ROCm (6.4.3 as of this writing) has preliminary rocBLAS support for Strix Halo gfx1151, but it is much slower and buggier (and doesn't have hipBLASlt kernels). For the most up-to-date support, currently it's recommended to use the latest gfx1151 [TheRock/ROCm "nightly" release](https://github.com/ROCm/TheRock/blob/main/RELEASES.md). These can be found at [https://therock-nightly-tarball.s3.amazonaws.com/](https://therock-nightly-tarball.s3.amazonaws.com/) (find the filename) or you can use the helper scripts described in the [Releases page]((https://github.com/ROCm/TheRock/blob/main/RELEASES.md)).
+
The latest release version of ROCm (6.4.3 as of this writing) has both rocBLAS and hipBLASlt support for Strix Halo gfx1151. For the most up-to-date builds, you can also install the latest gfx1151 [TheRock/ROCm "nightly" release](https://github.com/ROCm/TheRock/blob/main/RELEASES.md). These can be found at [https://therock-nightly-tarball.s3.amazonaws.com/](https://therock-nightly-tarball.s3.amazonaws.com/) (find the filename) or you can use the helper scripts described in the [Releases page]((https://github.com/ROCm/TheRock/blob/main/RELEASES.md)).
### Performance Tips
- If you are not using VFIO or any type of GPU passthrough, you should set `amd_iommu=off` in your kernel options for ~6% faster memory reads (actuall impact on llama.cpp tg performance tends to be smaller, about <2%. Note that when tested, `iommu=pt` does not give any speed benefit.