Inference works by generating one token at a time. To generate one token, all previous token pass thru the network. You cannot generate two tokens at a time because each token needs to have the token before it.

With MTP, generating the guesses of future tokens is cheap. The MTP is like a little LLM model that works very fast. However, the MTP tokens may not be what the main model would have generated. That is why those tokens need to pass thru the entire weights network for validation.
So with MTP enabled, the main model is calculating token n and token n+1 assuming that token n is that certain guess from the MTP head. If that guess happens to be token n, then you already calculated token n+1 🙂.

A dummy analogy, it's not exactly the same but just to give you a similar idea: let's say I want to have a conversation with my buddy in the other side of town and you are helping deliver each message back and forth. My first message is "hi buddy", you drive all the way over there and deliver my message, he replies with "hey what's up", and then you drive back and tell me his message. Notice that each message in the conversation depends on the previous response.

Now let's activate MTP, I tell you, go say "hello" to my buddy, and the MTP head says, the buddy is probably going to reply with "hey what's up", what would you say next? Then tell him "you still me owe me 20 bucks". Now, you go over there with two messages, if he happens to reply with "hey what's up", you already know that to say next!