[Doc] Add Parallel Draft Models

docs/features/speculative_decoding/README.md

@@ -6,11 +6,12 @@ To train your own draft models for optimized speculative decoding, see [vllm-pro
 
 ## vLLM Speculation Methods
 
-vLLM supports a variety of methods of speculative decoding. Model-based methods such as EAGLE, MTP, draft models, and MLP provide the best latency reduction, while simpler methods such as n-gram and suffix decoding provide modest speedups without increasing workload during peak traffic.
+vLLM supports a variety of methods of speculative decoding. Model-based methods such as EAGLE, MTP, draft models, PARD and MLP provide the best latency reduction, while simpler methods such as n-gram and suffix decoding provide modest speedups without increasing workload during peak traffic.
 
 - [EAGLE](eagle.md)
 - [Multi-Token Prediction (MTP)](mtp.md)
 - [Draft Model](draft_model.md)
+- [Parallel Draft Model (PARD)](parallel_draft_model.md)
 - [Multi-Layer Perceptron](mlp.md)
 - [N-Gram](n_gram.md)
 - [Suffix Decoding](suffix.md)
@@ -25,6 +26,7 @@ depend on your model family, traffic pattern, hardware, and sampling settings.
 | EAGLE | High gain | Medium to high gain | Strong general-purpose model-based method. |
 | MTP | High gain | Medium to high gain | Best when the target model has native MTP support. |
 | Draft model | High gain | Medium gain | Needs a separate draft model. |
+| Parallel Draft Model | High gain | Medium to high gain | Low draft model latency. |
 | MLP speculator | Medium to high gain | Medium gain | Good when compatible MLP speculators are available. |
 | N-gram | Low to medium gain | Medium gain | Lightweight and easy to enable. |
 | Suffix decoding | Low to medium gain | Medium gain | No extra draft model; dynamic speculation depth. |

docs/features/speculative_decoding/parallel_draft_model.md

@@ -0,0 +1,46 @@
+# Parallel Draft Models
+
+The following code configures vLLM to use speculative decoding where proposals are generated by [PARD](https://arxiv.org/pdf/2504.18583) (Parallel Draft Models).
+
+## PARD Offline Mode Example
+
+```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="Qwen/Qwen3-8B",
+    tensor_parallel_size=1,
+    speculative_config={
+        "model": "amd/PARD-Qwen3-0.6B",
+        "num_speculative_tokens": 12,
+        "method": "draft_model",
+        "parallel_drafting": True,
+    },
+)
+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}")
+```
+
+## PARD Online Mode Example
+
+```bash
+vllm serve Qwen/Qwen3-4B \
+    --host 0.0.0.0 \
+    --port 8000 \
+    --seed 42 \
+    -tp 1 \
+    --max_model_len 2048 \
+    --gpu_memory_utilization 0.8 \
+    --speculative_config '{"model": "amd/PARD-Qwen3-0.6B", "num_speculative_tokens": 12, "method": "draft_model", "parallel_drafting": true}'
+```
+
+## Pre-trained PARD weights
+
+- [amd/pard](https://huggingface.co/collections/amd/pard)

pyproject.toml

@@ -175,6 +175,8 @@ tme = "tme"
 dout = "dout"
 Pn = "Pn"
 arange = "arange"
+PARD = "PARD"
+pard = "pard"
 
 [tool.typos.type.py]
 extend-glob = []