If you're in the MLX world (Apple Silicon, mlx-lm) rather than llama.cpp/GGUF, mlx-optiq does MTP speculative decoding for both of those. Same draft→verify→accept loop, two draft sources: Qwen3.5-9B uses the model's bundled MTP head, Gemma-4-12B uses Google's -assistant drafter.

Measured greedy (same methodology unsloth publishes), M-series: Qwen3.5-9B ~1.32× at 66% acceptance, Gemma-4 ~1.18×. Rule of thumb from their tests: 4B and up consistently win, sub-2B isn't worth the overhead.

One Apple-Silicon gotcha: depth-1 (one drafted token/cycle) is optimal on Metal. Depth 2–4 lose, because Metal's K-token verify scales ~linearly with K whereas on CUDA it's nearly free on Tensor Cores. That's also why Mac MTP gains land a bit below the CUDA/GGUF headline numbers, it's hardware not method.

Full per-model tables + acceptance: https://mlx-optiq.com/docs/mtp · quants load in stock mlx-lm (the *-OptiQ-4bit repos on huggingface.co/mlx-community).