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Comment by puilp0502 | Hacker News Reader

What kind of benefit does Multi-Token Prediction bring to the inference side? Is it only relevant in pretraining efficiency?

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Speculative decoding! It makes inference a LOT faster.

Instead of generating tokens one at a time, you generate the second one as well, and then use speculative decoding on that second token (instead of having it be produced by a draft model like Qwen 0.6b). If the token is checked and is correct, then the 2nd token gets generated MUCH faster.

If it's wrong, you have to generate it again the normal way (a lot slower than just checking it). Usually, it's correct, so inference is a lot faster.

Because then the second token only needs to be checked, not generated, as it’s already generated? And it’s much faster to generate multiple tokens at the same time than one at a time? Is that the idea?

I’m not an expert on LLMs, just a user.

No, the parent is wrong.

Checking a token is the same as generating it.

The benefit however is in the next (third) token. After generating tokens 1 and 2 (in one turn), you start generating token 3 (and 4). You also get the “real” prediction for token 2. If the “real” prediction matches the MTP (Multi-Token Prediction) from previous turn, you have just generated 3 correct tokens (and another speculative). If not, you’ve now corrected token 2, but token 3 is wrong (it follows the wrong token 2) so you need ti generate it again.

Thanks for the clarification. Your comment made me connect the similarity (in spirit) of Speculative Decoding to Speculative Execution [1] in CPUs. Very cool and clever optimization strategy for LLMs, IMHO.

[1] https://en.wikipedia.org/wiki/Speculative_execution

Does it work to predict tokens 3 and 4 (or 5, 6) in the same way? I wonder how extreme the hit rate drop-off is.

To clarify, I should have stated: "Instead of generating tokens one at a time, you generate the second one as well WITH MTP, and then use speculative decoding on that second token (instead of having the second token be produced by a draft model like Qwen 0.6b). If the FIRST MTP token is checked and is correct, then the second token gets generated MUCH faster."

It relies on an “unintuitive observation”[0] that you can run batches basically for free (up to a limit). So if you only run one inference, you batch it plus a lot of guesses and, if you guess right, can speed up the inference by the number of guesses. If you guess wrong, you're back to regular speed (and still fully correct).

[0] https://x.com/karpathy/status/1697318534555336961

Basically you can generate the next two tokens at once in the same matmul, and rollback to one-at-a-time when your generation said you guessed wrong (as that will mean the second of your pair you generated was generated based on revoked context).

yes, if you know the sequence of tokens ahead of time you can verify them about as quickly as you can generate one more token because of the parallelism benefits.

If you don’t know the future tokens though, then you can’t, and blind guessing of tokens is infeasible because the vocabulary contains circa 100k possible different tokens.

Hmm but isn't the checking only required because the draft model is not the same model and can only speculate what the main one is thinking, hence the name? If the main model generates two tokens itself, then how can it be wrong about its own predictions?

Because if you generate token n+1 with all 48 layers of Qwen3-Next and 80 billion params, and also generate token n+2 with the 1 MTP layer at 2bil params... that n+2 token can be much lower quality than the n+1 token but mostly correct.

Let's say you have a model that generates the string "The 44th president of the United States is ___ ___". Your model will generate "Barack" as the n+1 token, and the MTP layer probably does a good enough job to generate "Obama" as the n+2 token (even though that MTP layer is a mere <2bil parameters in size). Then you just check if "Obama" is correct via the same speculative decoding process, which is a lot faster than if you had to start over from layer 1-48 and generate "Obama" the regular way.

> Then you just check if "Obama" is correct via the same speculative decoding process, which is a lot faster than if you had to start over from layer 1-48 and generate "Obama" the regular way.

That doesn't match my understanding of what speculative decoding does: AFAIK with regular speculative decoding you ask a smaller llm infer the next few tokens (let say 5 tokens) and then, you can have the big model infer token 1, 2, 3, 4, 5 and 6 in parallel (each time starting from the sentence partially completed by the smaller model). Because llms are bandwidth bound, doing the same work six times in parallel isn't slower than doing it only once (what's costly is moving the massive model weights between VRAM and the GPU cores).

If token 1,2 and 3 match what the small models inferred, then you keep them. As soon as you have a mismatched token (say token 4) it means that you have to discard the next inferred tokens (here token 5 and 6) because they were calculated under a wrong assumption for token 4.

So if the MTP layer merely replace the smaller llm in the previous scheme with everything else working the same way, you would save anything when inferring “Obama” (you'd still need to “generate it the regular way”, as there isn't really another way) but you could also start working on the word immediately after “Obama” by assuming “Obama” was already chose. And if the model actually outputted “Hussein” instead of “Obama”, then the token calculated to happen after “Obama” would have to be discarded.

Or maybe my understanding of speculative decoding is completely off…

Sounds right. The policy for rejection can depend on what you want - you might accept the top K highest probability tokens or top P probability mass. Or you can do something like importance sampling and probabilistically reject based on the ratio of likelihoods

If you ask me to guess an answer, I'll _usually_ produce the same answer as if I had time to think about it deeply, but not always...

I believe it's something along these lines. The MTP head runs simultaneously and generates a probability list based on what it thinks the results will be, learned during training.

If n+1 = "Barack" then n+2 = "Obama" (confidence: 0.90) If n+1 = "The" then n+2 = "quick" (confidence: 0.45) If n+1 = "President" then n+2 = "Biden" (confidence: 0.75)

A threshold is set (say, as 90%) so that if the n+2 prediction is above that (as in the first example) it uses it without having to determine it with the main model. It's confident "enough".

Well yeah; also inference benefits massively from batching, so you use the guesses to pre fill context needed to infer the next speculated tokens, and if the guesses were wrong, you just have to re-compute the speculated ones that depended on the guessed context.

You compute the next token and guess the one after; then you try to take the guess for real and compute the one after together with running inference for the guessed one, and the one after is speculated on the guess being correct.

the 2nd token is generated without knowing what token was chosen for the 1st token

> What kind of benefit does Multi-Token Prediction bring to the inference side? Is it only relevant in pretraining efficiency?

It is only useful for inference and doesn't help with pretraining. Which actually points to speculative decoding not being sufficiently general, as the same underlying property (some sequences of tokens are easy to predict) could be exploited for training as well. See here: https://goombalab.github.io/blog/2025/hnet-future/#d-footnot...

There is no reason that it couldn’t be beneficial for training though.

Except that speculative decoding is de facto only an inference time optimization. But the H-Net architecture from the previous reference, which doesn't require tokens or speculative decoding, does something similar both for inference and training.

Yes, but the discussion is about Multi-Token Prediction (Gloeckle et al. 2024) which is only incidentally useful for speculative decoding.

It could be a better draft model than separately trained EAGLE etc for speculative decoding.

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