A unified local model that both codes at the Qwen3.6-27B level and processes images/videos, packaged for Apple Silicon via MTPLX native speculative decoding with Multi-Token-Prediction (MTP).

This repository restores the multimodal vision tower that is typically stripped in text-only MLX/MTPLX conversions.

Architecture

Body: 4-bit affine, group size 64 (from Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed)
MTP sidecar (mtp/weights.safetensors): Prequantized MTP heads with a 3-bit affine group-64 draft-only LM head.
Vision tower (vision_tower.safetensors, 333 tensors, bf16 unquantized): Extracted verbatim from the official checkpoint's own model.visual.* weights (shards 7 and 8 of Qwen/Qwen3.6-27B) and renamed to the vision_tower.* prefix for MTPLX native VLM compatibility (~879 MB).

Usage (MTPLX)

bash

mtplx start --model <path-to-this-model-dir> --port 8092 \
  --chat-template-path <path-to-this-model-dir>/chat_template.jinja

Serves an OpenAI-compatible endpoint supporting both text and image input payloads via POST /v1/chat/completions.

The MTPLX CLI defaults to mtp_history_policy=committed, which this model requires for healthy deep-position acceptance. The model's recommended draft sampler is temperature 0.7 (see recommended_draft_sampler in mtplx_runtime.json); pass --draft-temperature 0.7 to match.

Performance (measured)

Apple M5 Pro 64 GB, MTPLX 1.0.4, temperature 0.6, mtp_history_policy=committed, --draft-temperature 0.7 (model recommended), thinking OFF, warm (4 warmup prompts discarded), 8 measured prompts from the calibration_coding suite, max_tokens=192, median tok/s.

Depth	tok/s (wall-clock e2e)	tok/s (decode-only)	speedup vs AR (e2e)	acceptance pos1/2/3
AR (`--no-mtp`)	13.7	14.5	1.00x	-
MTP depth 1	19.7	20.9	1.44x	0.923
MTP depth 2	19.9	20.8	1.46x	0.950 / 0.753
MTP depth 3	18.1	19.7	1.32x	0.900 / 0.759 / 0.648

Two throughput definitions are reported:

tok/s (wall-clock e2e) = generated_tokens / total_elapsed, includes prompt prefill. The real end-to-end speed and the honest denominator for the speedup ratio.
tok/s (decode-only) = generated_tokens / decode_elapsed, excludes prefill.

Acceptance is stable across runs: greedy draft (temp 0.0) gives 0.874/0.740/0.636, recommended draft (temp 0.7) gives 0.900/0.759/0.648, both within noise. Vision graft has zero acceptance cost relative to the text-only source (Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed measures identical acceptance 0.874/0.740/0.636 under the same config).

Earlier baked mtplx_runtime.json figures (acceptance 1.0/0.98/0.94, ~63 tok/s) were measured on Apple M5 Max 128 GB and are not reproducible on M5 Pro 64 GB due to lower memory bandwidth; they are preserved in the historical_m5max field of mtplx_runtime.json for traceability. The vision tower is lazy-loaded (only when a request carries an image), so text-only throughput is unaffected by it. Numbers are indicative, not a benchmark suite.

Sources And Attribution

Component	Source	Revision / License
Base model	`Qwen/Qwen3.6-27B`	Apache-2.0
Quantized Text/MTP repo	`Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed`	Apache-2.0
MTPLX conversion	`youssofal/mtplx`	Apache-2.0

Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision

Qwen3.6-27B MTPLX Optimized Speed (Vision-Enabled)

Architecture

Usage (MTPLX)

Performance (measured)

Sources And Attribution

Jonandrop/
Qwen3.6-27B-MTPLX-Optimized-Speed-Vision