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MTP models share internal state with the main model, and also refer to parameters in the model.

They are more like a single model that has two separate attention head mechanisms.

Being grafted onto the main model reduces layer duplication that you’d otherwise have: at least for Step and Qwen 3.6

Step 2.7’s MTP seems broken (at least for ik_llama.cpp) where the draft model starts and ends in block 3 but ik_llama bails out looking for block 0 :(

Aw that’s a shame; I’m running the official llama.cpp on my Spark-alike, and it works great now. Proper triple head too which is what it is trained on, gets me up to 35-40tk/s decode

I mean just like GGUFs aren't technically necessary yet are _way_ more convenient than using Safetensors and configuring the default Jinja prompt by-hand, it makes sense to bundle the draft model too. For all intents and purposes, the only people who will train a draft model are the people who train the original model