Multi-token prediction (MTP) can make language-model generation faster by letting a smaller drafter propose several upcoming tokens and having the main model verify them together. Accepted draft tokens can be emitted after one verification pass; rejected ones are replaced by the target model’s own prediction. The method preserves the target model as the authority, but its speed benefit depends on the model, hardware, serving software, and workload.
How does multi-token prediction work?
In ordinary autoregressive decoding, a model predicts one token, then uses that token to predict the next. Because each step depends on the previous one, the target model must run repeatedly in sequence.
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Speculative decoding changes the sequence of work. A drafter proposes a short continuation, and the target model evaluates the proposed positions together. When the target accepts draft tokens, it can emit multiple tokens from that verification work. If it rejects a token, the target supplies its own token at that position, and drafting proceeds from the corrected continuation. The draft is a proposal, not an instruction the target must follow.
This approach aims to reduce the number of expensive, serial target-model passes. It does not guarantee that a model will generate faster: drafting and verification also consume compute, and the benefit depends on how many proposed tokens the target can accept and how efficiently the system runs them.
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Which Qwen and Gemma models have documented MTP support?
Qwen: support is model-specific
The vLLM Speculators guide lists Qwen3-Next and Qwen3.5 as models with native MTP support. Its description says the MTP head combines verifier hidden states with token embeddings, normalizes and projects them, then passes them through a decoder layer. Output states are fed forward recursively to draft later tokens.
That support should not be generalized to every model named Qwen3. The Qwen3 technical report, dated May 14, 2025, does not describe MTP. Check the specific checkpoint and serving framework documentation before assuming native MTP is available or planning a deployment.
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Gemma: Google documents MTP for Gemma 4
Google’s Gemma 4 MTP guide describes a smaller drafter that shares the target model’s input embedding table and builds on activations from the target’s last layer. Google also describes combining those activations with token embeddings and projecting them down to the drafter’s dimension.
For Gemma 4 E2B and E4B, Google describes an efficient embedder that clusters similar tokens, narrowing final calculations to likely clusters. These are Gemma 4 design details; they do not establish that every Gemma release uses the same MTP arrangement.
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How much faster can MTP be?
In a May 5, 2026 announcement, Google reported “up to a 3x speedup” for Gemma 4 MTP drafters. Google said it tested with LiteRT-LM, MLX, Hugging Face Transformers, and vLLM. This is a vendor-reported upper bound across tested configurations, not a result to expect on every model or machine.
Google also reported speedups of up to about 2.2x locally for Gemma 4 26B MoE on Apple Silicon at batch sizes of 4 to 8. At batch size one, the benefits can be limited: MoE verification may require loading additional expert weights, which can offset time saved by drafting. More sequences can improve expert reuse. Google’s guide cautions that the 26B A4B drafter may not yield speedups on hardware without good parallelism.
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These results illustrate why an MTP speed claim needs its conditions attached. Architecture, hardware, backend, and batch size all affect whether speculative work pays off. A drafter that adds overhead without enough accepted tokens—or a serving setup that cannot overlap work efficiently—may not improve throughput or latency.
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The cited sources do not provide a controlled benchmark comparing Qwen and Gemma MTP on the same hardware and prompts. They therefore do not establish which family is faster or produces better results under equivalent conditions.
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For a useful comparison, keep the target task and prompt, hardware, precision and memory settings, serving framework and version, batch size, and output length constant. Measure both tokens per second and end-to-end latency. If the system exposes them, record draft acceptance rate or accepted draft length as well: these help show whether a speed difference comes from effective drafting or from other parts of the serving stack.
Where can you try MTP?
Google says Gemma 4 MTP weights are available through Hugging Face and Kaggle under the same Apache 2.0 license as Gemma 4. Its announcement lists Transformers, MLX, vLLM, SGLang, and Ollama as ways to experiment. Availability and compatibility depend on the particular model and software versions.
For Qwen native MTP, vLLM Speculators documents fine-tuning and deployment workflows for supported models. Consult its current model and framework documentation for the checkpoint and serving path you intend to use; support for one model does not imply support for every variant.
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