DeepSeek's DSpark speculative decoding framework, open-sourced on June 27, lifts per-user generation speed on data-center GPUs by 60%–85%, but the official implementation doesn't cover Apple Silicon. Independent engineer Abdur Rahim published mlx-dspark (v0.1.0, July 2) on GitHub, the first to natively port DSpark along with z-lab's DFlash to MLX, and ran out "byte-for-byte identical" strictly lossless output. In the M4 Pro measurement, Gemma-4 12B's generation speed goes from 18.4 tok/s to about 30 tok/s, Qwen3-4B from 52.9 tok/s to about 73 tok/s, acceleration ratios about 1.6× and 1.4×. The draft model uses 4-bit quantization (1.8 GB), the target model defaults to 8-bit, fully utilizing the M-series unified memory. What's more interesting is that DSpark and DFlash did a head-to-head in the same verify loop: in high-acceptance scenarios like code and math, DFlash's full 16-block diffusion can hit about 2.1× (36 tok/s); in open chat where the acceptance rate doesn't go up, DSpark's Markov head instead overtakes. One package can switch two draft strategies by task, this is a first on local Mac. But the author points out Mac's ceiling: Gemma-4 12B spends about 14 ms more per multi-core verified token, the acceleration ratio upper limit is nailed at around 2.2×. DSpark's service-side story doesn't reproduce on a laptop, but "lossless + consumer-grade runnable" itself is a clear signal that edge LLM inference is maturing.