update eagle3 related document for qwen3

Signed-off-by: bhsueh <[email protected]>

Author: byshiue

examples/models/core/qwen/README.md

@@ -26,6 +26,7 @@ This document shows how to build and run a [Qwen](https://huggingface.co/Qwen) m
     - [Serving](#serving)
       - [trtllm-serve](#trtllm-serve)
       - [Disaggregated Serving](#disaggregated-serving)
+    - [Eagle3](#eagle3)
   - [Dynamo](#dynamo)
   - [Notes and Troubleshooting](#notes-and-troubleshooting)
   - [Credits](#credits)
@@ -891,6 +892,38 @@ Note that the optimal disaggregated serving configuration (i.e. tp/pp/ep mapping
 on the request parameters, the number of concurrent requests and the GPU type. It is recommended to experiment to identify optimal
 settings for your specific use case.
 
+#### Eagle3
+
+Qwen3 now supports Eagle3 (Speculative Decoding with Eagle3). To enable Eagle3 on Qwen3, you need to set the following arguments when running `trtllm-bench` or `trtllm-serve`:
+
+- `speculative_config.decoding_type: Eagle`
+  Set the decoding type to "Eagle" to enable Eagle3 speculative decoding.
+- `speculative_config.max_draft_len: 3`
+  Set the maximum number of draft tokens generated per step (this value can be adjusted as needed).
+- `speculative_config.speculative_model_dir: <EAGLE3_DRAFT_MODEL_PATH>`
+  Specify the path to the Eagle3 draft model (ensure the corresponding draft model weights are prepared).
+
+Currently, there are some limitations when enabling Eagle3:
+
+1. `attention_dp` is not supported. Please disable it or do not set the related flag (it is disabled by default).
+2. If you want to use `enable_block_reuse`, the kv cache type of the target model and the draft model must be the same. Since the draft model only supports fp16/bf16, you need to disable `enable_block_reuse` when using fp8 kv cache.
+
+Example `extra-llm-api-config.yml` snippet for Eagle3:
+
+```bash
+echo "
+enable_attention_dp: false
+speculative_config:
+    decoding_type: Eagle
+    max_draft_len: 3
+    speculative_model_dir: <EAGLE3_DRAFT_MODEL_PATH>
+kv_cache_config:
+    enable_block_reuse: false
+" >> ${path_config}
+```
+
+For further details, please refer to [speculative-decoding.md](../../../../docs/source/advanced/speculative-decoding.md)
+
 ### Dynamo
 
 NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.