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Chapter 2. New features and enhancements
This section describes new features and enhancements in Red Hat OpenShift AI.
2.1. New Features
- Customizable serving runtime parameters
- You can now pass parameter values and environment variables to your runtimes when serving a model. Customization of the runtime parameters is particularly important in GenAI use cases involving vLLM.
- Support for deploying quantized models
-
You can use the vLLM ServingRuntime for KServe runtime to deploy models that are quantized for the Marlin kernel. If your model is quantized for Marlin, vLLM automatically uses the Marlin kernel based on the underlying hardware. For other quantized models, you can use the
--quantization=marlincustom parameter. For information about supported hardware, see Supported Hardware for Quantization Kernels on the vLLM website. - code-server workbench image
The code-server workbench image included in Red Hat OpenShift AI, previously available as a Technology Preview feature, is now generally available. For more information, see Working in code-server.
With the code-server workbench image, you can customize your workbench environment by using a variety of extensions to add new languages, themes, debuggers, and connect to additional services. You can also enhance the efficiency of your data science work with syntax highlighting, auto-indentation, and bracket matching.
NoteElyra-based pipelines are not available with the code-server workbench image.
2.2. Enhancements
- Custom connection types
- Administrators can use the enhanced connections feature to configure custom connections to data sources such as databases, making it easier for users to access data for model development. Additionally, users can access models from repositories such as Hugging Face for model serving, thanks to the built-in connection type for URI-based repositories.
- NVIDIA Triton Inference Server version 24.10 runtime: additional models tested and verified
The NVIDIA Triton Inference Server version 24.10 runtime has been tested with the following models for both KServe (REST and gRPC) and ModelMesh (REST):
- Forest Inference Library (FIL)
- Python
- TensorRT
- Distributed workloads: additional training images tested and verified
Several additional training images are tested and verified:
ROCm-compatible KFTO cluster image
A new ROCm-compatible KFTO cluster image,
quay.io/modh/training:py311-rocm61-torch241, is tested and verified. This image is compatible with AMD accelerators that are supported by ROCm 6.1.ROCm-compatible Ray cluster images
The ROCm-compatible Ray cluster images
quay.io/modh/ray:2.35.0-py39-rocm61andquay.io/modh/ray:2.35.0-py311-rocm61, previously available as a Developer Preview feature, are tested and verified. These images are compatible with AMD accelerators that are supported by ROCm 6.1.CUDA-compatible KFTO image
The CUDA-compatible KFTO cluster image, previously available as a Developer Preview feature, is tested and verified. The image is now available in a new location:
quay.io/modh/training:py311-cuda121-torch241. This image is compatible with NVIDIA GPUs that are supported by CUDA 12.1.
These images are AMD64 images, which might not work on other architectures. For more information about the latest available training images in Red Hat OpenShift AI, see Red Hat OpenShift AI Supported Configurations.
