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.

Gemma 4 Assistant Models¶

Gemma 4 assistant checkpoints use vLLM's Gemma 4 MTP path. They are not generic draft models, even though they are passed through the model field in --speculative-config.

Use "method": "mtp" when serving Gemma 4 with an assistant checkpoint:

vllm serve google/gemma-4-E2B-it \
    --tensor-parallel-size 1 \
    --max-model-len 8192 \
    --speculative-config '{"method":"mtp","model":"gg-hf-am/gemma-4-E2B-it-assistant","num_speculative_tokens":1}'

The E2B, E4B, 26B-A4B, and 31B Gemma 4 IT assistant checkpoints are supported when their configuration uses model_type: gemma4_assistant. vLLM maps those checkpoints to Gemma4MTPModel internally and wires the assistant layers to share KV cache with the target model.

If an older vLLM release logs SpeculativeConfig(method='draft_model', ...) for a Gemma 4 assistant checkpoint, that release is treating the assistant as a generic draft model and may fail during initialization for multimodal Gemma 4 targets. Upgrade to a version with Gemma 4 MTP support instead.

Offline Example¶

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¶

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.