Merge pull request #417 from intel/uMartinXu-patch-10

Update README.md

Author: vbedida79

README.md

@@ -13,7 +13,7 @@ This project delivers reference infrastructures powered by Intel AI hardware and
 
 The recommended **Infrastructure Cluster** is built with [**Intel® scalable Gaudi® Accelerator**](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html#gaudi-architecture) and standard servers. The [Intel® Xeon® processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/xeon6-product-brief.html ) are used in these Gaudi servers as worker nodes and in standard servers as highly available control plane nodes. This infrastructure is designed for **high availability**, **scalability**, and **efficiency** in **Retrieval-Augmented Generation (RAG) and other Large Language Model (LLM) inferencing** workloads.
 
-The [**Gaudi embedded RDMA over Converged Ethernet (RoCE) network**](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Scaling_Guide/Theory_of_Distributed_Training.html#theory-of-distributed-training), along with the [**3 Ply Gaudi RoCE Network topology**](https://docs.habana.ai/en/latest/Management_and_Monitoring/Network_Configuration/Configure_E2E_Test_in_L3.html#generating-a-gaudinet-json-example) supports high-throughput and low latency LLM Parallel Pre-training and Post-training workloads, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). For more details, see: [Training and fine-tuning LLM Models with Intel Enterprise AI Foundation on OpenShift](https://github.com/intel/intel-technology-enabling-for-openshift/wiki/Fine-tunning-LLM-Models-with-Intel-Enterprise-AI-Foundation-on-OpenShift)
+The [**Gaudi embedded RDMA over Converged Ethernet (RoCE) network**](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Scaling_Guide/Theory_of_Distributed_Training.html#theory-of-distributed-training), along with the [**Three Ply Gaudi RoCE Network topology**](https://docs.habana.ai/en/latest/Management_and_Monitoring/Network_Configuration/Configure_E2E_Test_in_L3.html#generating-a-gaudinet-json-example) supports high-throughput and low latency LLM Parallel Pre-training and Post-training workloads, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). For more details, see: [LLM Post‐Training Solution with Intel Enterprise AI Foundation for OpenShift](https://github.com/intel/intel-technology-enabling-for-openshift/wiki/Fine-tunning-LLM-Models-with-Intel-Enterprise-AI-Foundation-on-OpenShift)
 
 This highly efficient infrastructure has been validated with cutting-edge enterprise AI workloads on the production-ready OpenShift platform, enabling users to easily evaluate and integrate it into their own AI environments.
 
@@ -32,7 +32,7 @@ Provisioning AI accelerators and networks on a scalable OpenShift/Kubernetes clu
 *	[**Kernel module management (KMM) operator**](https://github.com/rh-ecosystem-edge/kernel-module-management) manages the deployment and lifecycle of out-of-tree kernel modules like Intel® Data Center GPU Driver for OpenShift*
 *	[**Machine config operator (MCO)**](https://github.com/openshift/machine-config-operator) provides an unified interface for the other general operators to configure the Operating System running on the OpenShift nodes.
 *	[**Node Feature Discovery (NFD)**](https://docs.redhat.com/en/documentation/openshift_container_platform/4.18/html/specialized_hardware_and_driver_enablement/psap-node-feature-discovery-operator) Operator detects and labels AI hardware features and system configurations. These labels are then used by other general operators.
-*	[**Intel® Converged AI Operator**]() will be used in the future to simplify the usage of the general operators to provision Intel AI features as a stable and single-entry point.
+*	[**Converged AI Operator**]() will be used in the future to simplify the usage of the general operators to provision Intel AI features as a stable and single-entry point.
 
 The Other general Operators can be added in the future to extend the AI features.
 
@@ -41,7 +41,7 @@ Intel and Red Hat have coordinated for years to deliver a production-quality ope
 
 The **Red Hat AI portfolio**, powered by **Intel AI technologies**, now includes:
 * [**Red Hat AI Inference Server**](https://www.redhat.com/en/about/press-releases/red-hat-unlocks-generative-ai-any-model-and-any-accelerator-across-hybrid-cloud-red-hat-ai-inference-server) leverages the [LLM-d](https://github.com/llm-d/llm-d) and [vLLM](https://github.com/vllm-project/vllm) projects, integrating with Llama Stack, Model Context Protocol (MCP), and the Open AI API to deliver standardized APIs for developing and deploying [OPEA-based](https://github.com/opea-project) and other production-grade GenAI applications scalable across edge, enterprise and cloud environments.
-* **Red Hat OpenShift AI Distributed Training** provides pre-training, SFT and RL for major GenAI foundation models at scale. With seamless integration of the Kubeflow Training Operator, Intel Gaudi Computing and RoCE Networking technology, enterprises can unlock the full potential of cutting-edge GenAI technologies to drive innovation in their domains.  See [Training and fine-tuning LLM Models with Intel Enterprise AI Foundation on OpenShift](https://github.com/intel/intel-technology-enabling-for-openshift/wiki/Fine-tunning-LLM-Models-with-Intel-Enterprise-AI-Foundation-on-OpenShift).
+* **Red Hat OpenShift AI Distributed Training** provides pre-training, SFT and RL for major GenAI foundation models at scale. With seamless integration of the Kubeflow Training Operator, Intel Gaudi Computing and RoCE Networking technology, enterprises can unlock the full potential of cutting-edge GenAI technologies to drive innovation in their domains.  See [LLM Post‐Training Solution with Intel Enterprise AI Foundation for OpenShift](https://github.com/intel/intel-technology-enabling-for-openshift/wiki/Fine-tunning-LLM-Models-with-Intel-Enterprise-AI-Foundation-on-OpenShift).
 * The operators to integrate [Intel Gaudi Software](https://docs.habana.ai/en/latest/index.html) or [OneAPI-based](https://www.intel.com/content/www/us/en/developer/tools/oneapi/overview.html#gs.kgdasr) AI software into OpenShift AI
 
 ## Releases and Supported Platforms