This week on Reddit's LocalLLaMA board, we noticed user frankentriple doing something worth paying attention to: they found that open-source model Ornith 1.5 35B had an undertrained "acceleration component" (MTP head — multi-token prediction head, which lets the model guess several tokens at once), removed it, and grafted on a version trained elsewhere. Same-task completion time dropped from 21 seconds to 14 — a 33% speedup. We think this matters: it tells us the local AI community's technical depth is stronger than most people assume.
What this is
Ornith is an open-source large model — 35B total parameters, 3B active (a Mixture-of-Experts MoE architecture that only fires a subset per inference), runnable on a standard gaming GPU. After version 1.5 shipped, the community noticed the MTP head was untrained — the equivalent of a car with an untuned turbo. frankentriple located trained MTP weights elsewhere and grafted them onto the APEX version (a model-compression format).
The result was counterintuitive: tokens generated per second rose from 60 to 64 (+3%), yet end-to-end task time dropped by a third. The reason: MTP lets the model predict the next step more accurately, cutting rework. This user runs AI to control a HAM amateur radio setup — for them, that speed delta is the difference between a responsive device and a sluggish one.
Industry view
On the positive side: we see the local AI community doing the work big labs won't. MTP training, tuning, porting — unsexy, no PR value, but it transforms a model from "runs" to "actually usable." That a user can pull this off tells us the ecosystem is mature enough.
On the case against and the risks: we want a question mark here. A 33% drop in wall-clock time against only a 3% rise in tokens is too wide a gap — the measurement may be sloppy, or it may simply be one task that happened to benefit. "Grafting MTP heads" is experimental; it may not transfer to other models. Ornith is a small community project, still a long way from mainstream open-source models like Llama or Qwen. One case does not a trend make.
Impact on regular people
For enterprise IT: limited reference value right now. But for latency-sensitive, cloud-averse scenarios — industrial control, embedded systems — local small models plus community optimization are starting to look viable.
For individual careers: running a 35B model is still something only a minority can do. But the "hands-on barrier" for open-source AI keeps falling. A year or two out, local AI tools showing up in a typical white-collar workflow isn't impossible.
For the consumer market: consumers won't notice for now. But the more mature local AI becomes, the tighter the pricing room cloud AI vendors have — that's the indirect effect.