diff --git a/docs/source/deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.md b/docs/source/deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.md
index dd9899e2a3..aa739468bb 100644
--- a/docs/source/deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.md
+++ b/docs/source/deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.md
@@ -212,6 +212,20 @@ These options provide finer control over performance and are set within a YAML f
 
 See the [`TorchLlmArgs` class](https://nvidia.github.io/TensorRT-LLM/llm-api/reference.html#tensorrt_llm.llmapi.TorchLlmArgs) for the full list of options which can be used in the `extra_llm_api_options`.
 
+### Wide Expert Parallelism
+
+Add the following fields to the YAML configuration file `/tmp/config.yml` to enable wide EP:
+```yaml
+moe_config:
+    backend: WIDEEP
+    max_num_tokens: 9216
+    load_balancer:  # configure online EP balancer
+      num_slots: 288
+      layer_updates_per_iter: 1
+```
+
+Refer to the wide EP [examples](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/wide_ep) for more details.
+
 ## Testing API Endpoint
 
 ### Basic Test
diff --git a/examples/wide_ep/README.md b/examples/wide_ep/README.md
index 4d2453bf73..acb0dfc102 100644
--- a/examples/wide_ep/README.md
+++ b/examples/wide_ep/README.md
@@ -19,7 +19,19 @@ Wide-EP solves these challenges through:
 
 ## Quick Start
 
-### 1. Configurations
+### Prerequisites
+
+* GPU: GB200 NVL72, H20, or RTX 6000D.
+* OS: Linux
+* Drivers: CUDA Driver 575 or Later
+* Docker with NVIDIA Container Toolkit installed
+* Python3 and python3-pip (Optional, for accuracy evaluation only)
+
+For GB200 NVL72, to make sure that Multi-Node NVLink (MNNVL) is correctly setup, check if the path `/dev/nvidia-caps-imex-channels` exists in the container. If the path doesn't exist, mount it when launching the Docker container.
+
+For more information on NVIDIA IMEX service for NVLink networks, refer to https://docs.nvidia.com/multi-node-nvlink-systems/imex-guide/overview.html.
+
+### Configurations
 
 An example yaml file to enable wide EP:
 ```yaml
@@ -35,12 +47,15 @@ moe_config:
 | `max_num_tokens` | If set, at most max_num_tokens tokens will be sent to torch.ops.trtllm.fused_moe at the same time.  | `None` | If the number of tokens exceeds max_num_tokens, the input tensors will be split into chunks and a for loop will be used. |
 | `load_balancer` | Configuration for MoE load balancing | `None` | Set path to the yaml file |
 
-#### Load Balancer Configuration
+#### Online Load Balancer Configuration
 
-An example `moe_load_balancer.yaml` file to configure online EP balancer:
 ```yaml
-num_slots: 288
-layer_updates_per_iter: 1
+moe_config:
+    backend: WIDEEP
+    max_num_tokens: 9216
+    load_balancer:
+        num_slots: 288
+        layer_updates_per_iter: 1
 ```
 
 | Parameter | Description | Default | Notes |
@@ -48,9 +63,13 @@ layer_updates_per_iter: 1
 | `num_slots` | Total number of expert slots | `None` | Must be ≥ total experts |
 | `layer_updates_per_iter` | Number of layers updated per iteration | `0` | `0` = offline, `>0` = online |
 
-Refer to the [ep_load_balancer](./ep_load_balancer/) directory for more details on EP load balancer.
+#### Offline Load Balancer Configuration
 
-### 2. Execute Wide-EP on SLURM Clusters
+Refer to the [Offline EP Load Balancer](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/wide_ep/ep_load_balancer#offline-ep-load-balancer) documentation.
+
+*Online EP Load Balancer is more suitable for production deployment needs to react timely to the online traffic changes.*
+
+### Execute Wide-EP on SLURM Clusters
 
 Refer to the [slurm_scripts](./slurm_scripts/) directory, which reuses [disaggregated slurm scripts](../disaggregated/slurm/) to automatically generate configuration files and submit jobs to SLURM clusters.
 
@@ -70,13 +89,32 @@ If `never` is highlighted, enable Transparent HugePages by the following command
 echo madvise > /sys/kernel/mm/transparent_hugepage/enabled
 ```
 
+### GB200 NUMA binding
+
+GPU memory are also on NUMA nodes on GB200 and system can also use that. Bind memory to CPU nodes to avoid GPU memory being used as host memory.
+```bash
+numactl -m 0,1 <command>
+```
+
+### Shared Memory Clean Up on EPLB
+
+To achieve online load balance, all expert weights are stored in shared host memory. 4 ranks on same GB200 node share the same expert weights to save memory. Normally, these shared host memory will be cleaned up at process exit, but they may not get chance to be cleaned if an abnormal exit happens.
+
+In that case, when seeing the following (or similar) error message:
+```
+FileExistsError: [Errno 17] File exists: '/moe_shared_l0_lr0_all'
+```
+you need to manually check `/dev/shm` directory and delete `/dev/shm/moe_shared_*` if any.
+
 ### Disaggregated serving related issues
 
 Refer to the [Troubleshooting and FAQ](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/advanced/disaggregated-service.md#troubleshooting-and-faq) section of Disaggregated-Service.
 
 ## References
 
-- [Technical Blog: Scaling Expert Parallelism in TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.md)
+- Technical Blog: Scaling Expert Parallelism in TensorRT-LLM
+  - [Part 1: Design and Implementation of Large-scale EP](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.md)
+  - [Part 2: Performance Status and Optimization](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog8_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.md)
 
 For detailed implementation examples and advanced usage, see the subdirectories:
 - [`ep_load_balancer/`](ep_load_balancer/): Load balancing tools and examples