The Vortigaunt (plain mixed quant) mixed-precision GGUF of Ornith-1.0-35B, with one addition: the multi-token-prediction (MTP) head from Qwopus-3.6-Coder surgically grafted on, enabling self-speculative decoding in ik_llama.cpp.

TL;DR: identical outputs to the plain mixed quant, but ~83 tok/s on code and ~80 tok/s on prose instead of ~78, on an 18 GB dual-GPU desktop. Free speed, verified token-by-token.

The head transplant

Ornith-1.0-35B ships without an MTP head — but Qwopus-Coder is a fine-tune of the same Qwen3.6-35B-A3B base, and its blk.40 nextn head (a full extra layer: attention + MoE + eh_proj/enorm/hnorm glue, ~0.55 GB) predicts the next-next token from hidden states that this model's hidden space is a close sibling of. So we graft it:

append the donor's 20 blk.40.* tensors after the target's 40 layers,
bump block_count 40 → 41,
set qwen35moe.nextn_predict_layers = 1.

Why this is safe: speculative decoding verifies every drafted token against this model. A foreign head can never change the output distribution — a bad match only lowers the acceptance rate (= less speedup). Measured across the series, acceptance tracks fine-tune distance from the donor: base Qwen3.6 93/90 (code/prose t/s) > abliterated 87/83

Ornith 83/80 > AgentWorld 82/79, all against a 78 t/s no-MTP baseline.

Recipe

Quant layout (same as the parent mixed quant): ffn_*_exps on blocks 13–26 → Q2_K with importance matrix (the CPU-offloaded set); everything else Q4_K; output-class Q6_K. ~4.9 bpw effective, ~18.7 GB.
Head: blk.40 nextn/MTP layer from Qwopus-Coder mixed-q2k (Q4_K experts), grafted byte-exact.
The mixed layout exists because decode on CPU-offload rigs is RAM-bandwidth-bound: only the offloaded-layer bytes matter, so those get Q2_K while GPU-resident tensors keep Q4_K quality.

Measured performance

Rig: RTX 3060 Ti 8 GB + RTX 3080 10 GB, DDR4, ik_llama.cpp, 128K ctx, greedy.

workload	tok/s	vs 78 t/s no-MTP baseline
code generation	83	+6%
prose	80	+3%

How to run

Requires ik_llama.cpp — its -mtp flag is what drives the nextn head (mainline llama.cpp loads the file but ignores the head).

bash

./llama-server -m ornith-1.0-35b-mixed-q2k-MTP.gguf \
  --jinja --cache-type-k q4_0 --cache-type-v q4_0 --flash-attn on \
  --ctx-size 131072 --parallel 1 --n-gpu-layers 99 --ctx-checkpoints 8 \
  -ot 'blk\.(1[3-9]|2[0-9])\.ffn_(up|gate|down)_exps\.weight=CPU' \
  --tensor-split 44,56 --ubatch-size 256 \
  -mtp --ctx-size-draft 8192 \
  --no-mmap --threads 8 --no-warmup

Notes for 18 GB-class rigs:

The MTP draft context costs VRAM, which is why this profile runs 128K ctx (not 256K), a small 8K draft context, and pins expert layers 13–29 to CPU (three more than the plain mixed profile). With less freed VRAM, flash-attention temp allocations OOM mid-decode. More VRAM → pin fewer layers and/or raise ctx.
--ctx-checkpoints 8 caps ik's dynamically allocated SSM checkpoints (default 32 × 64 MiB ≈ 2 GB at deep context — an OOM trap on long agent sessions).
Strong reasoner; give it room to think.
Drop -mtp (and the diet) and it behaves exactly like the parent mixed quant at 256K.

Credits

Base model: deepreinforce-ai/Ornith-1.0-35B (MIT)
MTP head donor: Jackrong/Qwopus3.6-35B-A3B-Coder-MTP-GGUF
Mixed quant, imatrix, transplant & profiling: xero0000
Series: Gordon (base) · Kleiner (coder) · G-Man (uncensored) · Vortigaunt (reasoner) · Alyx (agentic)

xero0000/Vortigaunt-Ornith-1.0-35B-mixed-q2k-MTP

⚡ Vortigaunt — Ornith-1.0-35B Mixed q2_K + Transplanted MTP Head

The head transplant

Recipe

Measured performance

How to run

Credits

xero0000/
Vortigaunt-Ornith-1.0-35B-mixed-q2k-MTP