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Swap new framework vs updated framework
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@@ -68,13 +68,6 @@ All KVM-based SR-IOV supported configurations require the GIM SR-IOV driver vers
ROCm provides a comprehensive ecosystem for deep learning development. For more information, see [Deep learning frameworks for ROCm](https://rocm.docs.amd.com/en/latest/how-to/deep-learning-rocm.html) and the [Compatibility
matrix](../../docs/compatibility/compatibility-matrix.rst) for the complete list of Deep learning and AI framework versions tested for compatibility with ROCm.
#### New frameworks
AMD ROCm has officially added support for the following Deep learning and AI frameworks:
* Ray is a unified framework for scaling AI and Python applications from your laptop to a full cluster, without changing your code. Ray consists of a core distributed runtime and a set of AI libraries for simplifying machine learning computations. It is currently supported on ROCm 6.4.1. For more information, see [Ray compatibility](https://advanced-micro-devices-rocm-internal--500.com.readthedocs.build/en/500/compatibility/ml-compatibility/ray-compatibility.html).
* llama.cpp is an open-source framework for Large Language Model (LLM) inference that runs on both central processing units (CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing a simple, dependency-free setup. It is currently supported on ROCm 6.4.0. For more information, see [llama.cpp compatibility](https://advanced-micro-devices-rocm-internal--500.com.readthedocs.build/en/500/compatibility/ml-compatibility/llama-cpp-compatibility.html).
#### Updated framework support
@@ -120,6 +113,15 @@ ROCm 7.0 enables support for ONNX Runtime 1.22.0.
ROCm 7.0 enables support for Triton 3.3.0.
#### New frameworks
AMD ROCm has officially added support for the following Deep learning and AI frameworks:
* Ray is a unified framework for scaling AI and Python applications from your laptop to a full cluster, without changing your code. Ray consists of a core distributed runtime and a set of AI libraries for simplifying machine learning computations. It is currently supported on ROCm 6.4.1. For more information, see [Ray compatibility](https://advanced-micro-devices-rocm-internal--500.com.readthedocs.build/en/500/compatibility/ml-compatibility/ray-compatibility.html).
* llama.cpp is an open-source framework for Large Language Model (LLM) inference that runs on both central processing units (CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing a simple, dependency-free setup. It is currently supported on ROCm 6.4.0. For more information, see [llama.cpp compatibility](https://advanced-micro-devices-rocm-internal--500.com.readthedocs.build/en/500/compatibility/ml-compatibility/llama-cpp-compatibility.html).
### Instinct Driver/ROCm packaging separation
The Instinct Driver is now distributed separately from the ROCm software stack and is stored under in its own location ``/amdgpu/`` in the package repository at [repo.radeon.com](https://repo.radeon.com/amdgpu/). The first release is designated as Instinct Driver version 30.10. See the [ROCm Gets Modular: Meet the Instinct Datacenter GPU Driver](https://rocm.blogs.amd.com/ecosystems-and-partners/instinct-gpu-driver/README.html) blog and [User and kernel-space support matrix](https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/reference/user-kernel-space-compat-matrix.html) for more information.