Local AI · Recipe · 1× Mac

Qwen3.8-27B with MLX on 1× Mac

Qwen3.8-27B with MLX on 1× Mac: 53.3 tok/s according to github.com (dataset as of Sep 29, 2026).

by Weschera

53.3tok/sEveryday

1 request, prose prompt, with MTP (nativ) (per recipe)

Source: github.com

Intelligence (original model) · Artificial Analysisno independent value

Instructions in the README Repo

Engine: oMLX 0.6.3rc2
Quantization: oQ4e (4-bit affine g64, 166 sens. Tensoren 5-bit; Stock-Qwen3.8-27B-Konversion)
Model family: Qwen3.8-27B
Context: 262,144
Parameters: 27B (dicht, lt. Modellname)
Creator: Weschera
GitHub stars: 64
Repo updated: Aug 21, 2026

Measurements

Every sourced value of this recipe, each with its condition and source. Bars relative to the largest value in the group.

Decode · 1 request

Everyday (decode)53.3 tok/sMittel aus 3 Reps, temp 0, thinking off, 320 gen. Tokens, ~2,1–2,4K-Token-Prompts; +11 % vs. 48.0 (oMLX 0.6.1) Source
Peak (decode)72.1 tok/sMTP-Acceptance Code 99,6 % (tok/cycle 3.76); Prose-Acceptance 78 % Source

Prefill by context

4K274 tok/sANE-Prefill (vorher ~83); raw JSON ane-mtp3-results.json im Repo Source

What you need

Hardware: 1 × Mac / Apple Silicon
Weights: Main weightsJundot/Qwen3.8-27B-oQ4e-mtp License unclear
Engine: oMLX 0.6.3rc2
Context: 262,144 tokens

Sources

github.com/Weschera/Qwen3.8-27B-oMLX-MTP-Mac/blob/main/README.md

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