diff --git a/docs/source/features/spec_decode.md b/docs/source/features/spec_decode.md
index 8c52c97a41e48..29f9a3b8a536b 100644
--- a/docs/source/features/spec_decode.md
+++ b/docs/source/features/spec_decode.md
@@ -159,6 +159,72 @@ A variety of speculative models of this type are available on HF hub:
 - [granite-7b-instruct-accelerator](https://huggingface.co/ibm-granite/granite-7b-instruct-accelerator)
 - [granite-20b-code-instruct-accelerator](https://huggingface.co/ibm-granite/granite-20b-code-instruct-accelerator)
 
+## Speculating using EAGLE based draft models
+
+The following code configures vLLM to use speculative decoding where proposals are generated by
+an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model.
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(
+    model="meta-llama/Meta-Llama-3-8B-Instruct",
+    tensor_parallel_size=4,
+    speculative_model="path/to/modified/eagle/model",
+    speculative_draft_tensor_parallel_size=1,
+)
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+```
+
+A few important things to consider when using the EAGLE based draft models:
+
+1. The EAGLE draft models available in the [HF repository for EAGLE models](https://huggingface.co/yuhuili) cannot be
+   used directly with vLLM due to differences in the expected layer names and model definition.
+   To use these models with vLLM, use the [following script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d) 
+   to convert them. Note that this script does not modify the model's weights.
+
+   In the above example, use the script to first convert
+   the [yuhuili/EAGLE-LLaMA3-Instruct-8B](https://huggingface.co/yuhuili/EAGLE-LLaMA3-Instruct-8B) model 
+   and then use the converted checkpoint as the draft model in vLLM.
+
+2. The EAGLE based draft models need to be run without tensor parallelism
+   (i.e. speculative_draft_tensor_parallel_size is set to 1), although
+   it is possible to run the main model using tensor parallelism (see example above).
+
+3. When using EAGLE-based speculators with vLLM, the observed speedup is lower than what is
+   reported in the reference implementation [here](https://github.com/SafeAILab/EAGLE). This issue is under
+   investigation and tracked here: [https://github.com/vllm-project/vllm/issues/9565](https://github.com/vllm-project/vllm/issues/9565).
+
+
+A variety of EAGLE draft models are available on the Hugging Face hub:
+
+| Base Model                                                           | EAGLE on Hugging Face                     | # EAGLE Parameters |
+|---------------------------------------------------------------------|-------------------------------------------|--------------------|
+| Vicuna-7B-v1.3                                                       | yuhuili/EAGLE-Vicuna-7B-v1.3             | 0.24B              |
+| Vicuna-13B-v1.3                                                      | yuhuili/EAGLE-Vicuna-13B-v1.3            | 0.37B              |
+| Vicuna-33B-v1.3                                                      | yuhuili/EAGLE-Vicuna-33B-v1.3            | 0.56B              |
+| LLaMA2-Chat 7B                                                       | yuhuili/EAGLE-llama2-chat-7B             | 0.24B              |
+| LLaMA2-Chat 13B                                                      | yuhuili/EAGLE-llama2-chat-13B            | 0.37B              |
+| LLaMA2-Chat 70B                                                      | yuhuili/EAGLE-llama2-chat-70B            | 0.99B              |
+| Mixtral-8x7B-Instruct-v0.1                                           | yuhuili/EAGLE-mixtral-instruct-8x7B      | 0.28B              |
+| LLaMA3-Instruct 8B                                                   | yuhuili/EAGLE-LLaMA3-Instruct-8B         | 0.25B              |
+| LLaMA3-Instruct 70B                                                  | yuhuili/EAGLE-LLaMA3-Instruct-70B        | 0.99B              |
+| Qwen2-7B-Instruct                                                    | yuhuili/EAGLE-Qwen2-7B-Instruct          | 0.26B              |
+| Qwen2-72B-Instruct                                                   | yuhuili/EAGLE-Qwen2-72B-Instruct         | 1.05B              |
+
+
 ## Lossless guarantees of Speculative Decoding
 
 In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of