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docs(ml): clarify cuda and openvino requirements (#9072)

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update docs
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docs/docs/features/ml-hardware-acceleration.md
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@ -10,15 +10,14 @@ You do not need to redo any machine learning jobs after enabling hardware accele
## Supported Backends
- ARM NN (Mali)
- CUDA (NVIDIA) Note: It is supported with [compute capability](https://developer.nvidia.com/cuda-gpus) 5.2 or higher
- OpenVINO (Intel)
- CUDA (NVIDIA GPUs with [compute capability](https://developer.nvidia.com/cuda-gpus) 5.2 or higher)
- OpenVINO (Intel discrete GPUs such as Iris Xe and Arc)
## Limitations
- The instructions and configurations here are specific to Docker Compose. Other container engines may require different configuration.
- Only Linux and Windows (through WSL2) servers are supported.
- ARM NN is only supported on devices with Mali GPUs. Other Arm devices are not supported.
- There is currently an upstream issue with OpenVINO, so whether it will work is device-dependent.
- Some models may not be compatible with certain backends. CUDA is the most reliable.
## Prerequisites
@ -36,9 +35,16 @@ You do not need to redo any machine learning jobs after enabling hardware accele
#### CUDA
- You must have the official NVIDIA driver installed on the server.
- The GPU must have compute capability 5.2 or greater.
- The server must have the official NVIDIA driver installed.
- The installed driver must be >= 535 (it must support CUDA 12.2).
- On Linux (except for WSL2), you also need to have [NVIDIA Container Runtime][nvcr] installed.
#### OpenVINO
- The server must have a discrete GPU, i.e. Iris Xe or Arc. Expect issues when attempting to use integrated graphics.
- Ensure the server's kernel version is new enough to use the device for hardware accceleration.
## Setup
1. If you do not already have it, download the latest [`hwaccel.ml.yml`][hw-file] file and ensure it's in the same folder as the `docker-compose.yml`.