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Speculative decoding can reduce the number of sequential steps a large language model (LLM) needs to generate text. A draft mechanism proposes several future tokens, and the target model checks them together, accepting an appropriate prefix. The work is not free: drafting, verification, and memory use still cost compute. The gain comes when those costs are outweighed by the target-model steps avoided.
What is speculative decoding?
Most LLMs generate text autoregressively: each next token depends on the tokens already produced, so the model repeatedly runs a forward pass to extend its output. This creates a serial bottleneck during decoding.
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Speculative decoding changes how those steps are scheduled. A draft mechanism proposes a short continuation; the larger target model evaluates the candidates in parallel and accepts a matching prefix. If a candidate is rejected, the decoding algorithm corrects course and continues. NVIDIA describes a rejection-sampling approach designed to preserve the target model’s output distribution while reducing sequential decoding steps (NVIDIA’s speculative decoding overview).
The method is most relevant when sequential decode latency is a bottleneck. It is not a universal speedup: draft overhead, accepted-prefix length, verification cost, workload, hardware, batch size, and serving implementation all affect the result.
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How does speculative decoding work?
- Draft: A lightweight drafter proposes one or more possible next tokens.
- Verify: The target model processes the proposed continuation in a batch rather than generating and checking each token in a separate serial round.
- Accept or correct: The algorithm accepts an appropriate prefix. If a proposal fails verification, it rejects or corrects the candidate and resumes from the valid point.
- Continue: The process repeats until the output is complete.
The key benefit is fewer serial target-model rounds, not the elimination of target-model computation. Verification still takes work, and the drafter adds its own cost.
What is MTP in LLMs?
MTP, or multi-token prediction, refers to predicting future token positions beyond the immediate next token. It can describe a training objective, an inference-time drafting mechanism, or both, depending on the model and serving stack.
MTP during training
DeepSeek-V3’s technical report describes training with future-token prediction targets. The report says this can densify training signals and may improve data efficiency or anticipation of future tokens. NVIDIA Megatron Core also documents MTP as a training and model-module concept (Megatron Core MTP documentation).
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MTP during inference
When a checkpoint includes compatible native MTP modules and the serving software supports them, those modules can draft future tokens for speculative decoding. DeepSeek-AI states that its MTP modules can be used for speculative decoding at inference time (DeepSeek-V3 Technical Report). MTP and speculative decoding are related but not synonymous: MTP can be a training technique, while speculative decoding is an inference strategy.
Why is guessing called “free”?
“Free” is shorthand for reducing serial target-model work. Several proposed tokens can be checked together, so accepted candidates may save repeated target-model decoding rounds. But the computation has not disappeared: the draft mechanism must produce candidates, the target must verify them, and the system must store and process the relevant state.
Whether the trade pays off depends on the balance between draft cost, verification cost, and the number of tokens accepted. If few proposed tokens are accepted, or verification and memory pressure are high, speculative decoding may offer little benefit or slow a workload down.
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Does speculative decoding change output quality?
In algorithms that verify drafts with an appropriate rejection-sampling procedure, the purpose is to preserve the target model’s output distribution while changing the decoding schedule. NVIDIA’s overview explains this guarantee for the method it describes; it should not be assumed for every approximate multi-token generation method. Check the algorithm and implementation used by a particular system rather than treating all forms of multi-token prediction as quality-equivalent.
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It is most promising when a workload is limited by sequential decode latency and the draft is cheap enough, and accurate enough, that accepted tokens save more target-model work than drafting and verification consume.
- Draft cost: A native MTP mechanism may avoid running a separate draft model, but it still uses compute. A separate drafter has its own latency and resource cost.
- Accepted-prefix length: Longer accepted runs can avoid more sequential target steps. Acceptance can vary by task and token position.
- Verification and memory: Checking candidates and maintaining model state can consume compute and GPU memory, including KV-cache capacity.
- Batch size and concurrency: In its documented TensorRT-LLM implementation, NVIDIA notes that speedups are observable at low batch sizes; this is implementation-specific, not a rule for every system (TensorRT-LLM speculative decoding documentation).
A 2026 preprint abstract also cautions that target verification can dominate execution and that acceptance length varies by output position, request, and dataset (2026 preprint abstract). That observation reinforces why a single acceptance rate or speed multiplier should not be assumed for other models or serving setups.
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What do reported MTP results show?
DeepSeek-AI’s 2024 DeepSeek-V3 report gives model-specific results: an 85–90% second-token acceptance rate across its evaluated generation topics, and 1.8 times as many tokens per second in its DeepSeek-V3 evaluation. These are results reported for that model and evaluation, not general rates or guaranteed speedups for MTP or speculative decoding (DeepSeek-V3 Technical Report).
NVIDIA’s latency example in its overview is illustrative, not an independent cross-vendor benchmark. Results from one model or implementation should not be treated as a promise for a different workload or serving stack.
How to evaluate an implementation
Compare the full decoding path under the workload you expect to serve—not just the drafter or its acceptance rate.
- Measure end-to-end latency and throughput with and without speculation on the same prompts, output lengths, hardware, and concurrency.
- Record draft latency, verification cost, accepted-prefix length, and how acceptance changes over token positions and request types.
- Include GPU memory use, KV-cache pressure, batch size, and the effect on concurrent requests.
- Confirm that the checkpoint has the required native MTP support, if applicable, and that the serving implementation supports that architecture.
Support changes over time and is model-specific. TensorRT-LLM’s rolling documentation lists MTP alongside EAGLE3, n-gram, draft-target, PARD, and DFlash methods, while noting that native MTP depends on supported model architectures and checkpoints (TensorRT-LLM documentation). The vLLM Speculators documentation likewise lists native MTP support for specified model families; verify its current compatibility information before choosing a checkpoint or serving version (vLLM Speculators documentation).
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