diff --git a/docs/features/speculative_decoding/README.md b/docs/features/speculative_decoding/README.md
index 899743c4e..ee6e0c895 100644
--- a/docs/features/speculative_decoding/README.md
+++ b/docs/features/speculative_decoding/README.md
@@ -6,14 +6,33 @@ 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, draft models, and mlp provide the best latency reduction, while simpler methods such as n-gram and 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, 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)
 - [Multi-Layer Perceptron](mlp.md)
 - [N-Gram](n_gram.md)
 - [Suffix Decoding](suffix.md)
 
+## Method Selection at a Glance
+
+Use this qualitative table as a starting point for method selection. Real gains
+depend on your model family, traffic pattern, hardware, and sampling settings.
+
+| Method | Low QPS (latency focused) | High QPS (throughput focused) | Notes |
+| --- | --- | --- | --- |
+| 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. |
+| 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. |
+
+For reproducible measurements in your environment, use
+[`examples/offline_inference/spec_decode.py`](../../../examples/offline_inference/spec_decode.py)
+or the [benchmark CLI guide](../../benchmarking/cli.md).
+
 ## Lossless guarantees of Speculative Decoding
 
 In vLLM, speculative decoding aims to enhance inference efficiency while maintaining accuracy. This section addresses the lossless guarantees of
diff --git a/docs/features/speculative_decoding/mlp.md b/docs/features/speculative_decoding/mlp.md
index 98a4d33e2..5b2647469 100644
--- a/docs/features/speculative_decoding/mlp.md
+++ b/docs/features/speculative_decoding/mlp.md
@@ -11,10 +11,10 @@ prompts = ["The future of AI is"]
 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
 llm = LLM(
-    model="meta-llama/Meta-Llama-3.1-70B-Instruct",
-    tensor_parallel_size=4,
+    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
+    tensor_parallel_size=1,
     speculative_config={
-        "model": "ibm-ai-platform/llama3-70b-accelerator",
+        "model": "ibm-ai-platform/llama3-8b-accelerator",
         "draft_tensor_parallel_size": 1,
         "method": "mlp_speculator",
     },
@@ -27,6 +27,12 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
+!!! warning "Known issue"
+    `ibm-ai-platform/llama3-70b-accelerator` can fail with:
+    `AttributeError: 'MLPSpeculatorConfig' object has no attribute 'num_attention_heads'`.
+    Track status in [#34106](https://github.com/vllm-project/vllm/issues/34106)
+    and [#34163](https://github.com/vllm-project/vllm/pull/34163).
+
 ## Pre-Trained MLP Drafter Models
 
 A variety of speculative models of this type are available on HF hub:
diff --git a/docs/features/speculative_decoding/mtp.md b/docs/features/speculative_decoding/mtp.md
new file mode 100644
index 000000000..bcd7153de
--- /dev/null
+++ b/docs/features/speculative_decoding/mtp.md
@@ -0,0 +1,50 @@
+# MTP (Multi-Token Prediction)
+
+MTP is a speculative decoding method where the target model includes native
+multi-token prediction capability. Unlike draft-model-based methods, you do not
+need to provide a separate draft model.
+
+MTP is useful when:
+
+- Your model natively supports MTP.
+- You want model-based speculative decoding with minimal extra configuration.
+
+## Offline 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="XiaomiMiMo/MiMo-7B-Base",
+    tensor_parallel_size=1,
+    speculative_config={
+        "method": "mtp",
+        "num_speculative_tokens": 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}")
+```
+
+## Online Example
+
+```bash
+vllm serve XiaomiMiMo/MiMo-7B-Base \
+    --tensor-parallel-size 1 \
+    --speculative_config '{"method":"mtp","num_speculative_tokens":1}'
+```
+
+## Notes
+
+- MTP only works for model families that support MTP in vLLM.
+- `num_speculative_tokens` controls speculative depth. A small value like `1`
+  is a good default to start with.
+- If your model does not support MTP, use another method such as EAGLE or draft
+  model speculation.