Outlier

Outlier

Ternary MoE + Apple Silicon quantization for on-device AI.
Desktop app for Mac. No token caps. Free forever.

outlier.host · Discord · Founders ($200 lifetime)


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Support the mission Founders lifetime $200 · 500-seat cap

What is Outlier?

A Mac-native AI platform with curated open-weights models for Apple Silicon. Built solo in 20 days on a Mac Studio, under $1,200 of compute spend. Three U.S. provisional patents filed on the underlying ternary MoE architecture.

Two tracks on HuggingFace:

  1. Research MoE — ternary Mixture-of-Experts overlays on frozen Qwen2.5 bases. 10B, 40B, 70B, 150B scales, MMLU-verified.
  2. Apple Silicon conversions — MLX 4-bit builds of strong open-weights models, tuned for the Outlier desktop app and usable standalone via mlx_lm.

Research line (MMLU verified)

All values at n=14,042 · lm-evaluation-harness v0.4.9.1 · bf16 5-shot · source weights unchanged since Day 13 (2026-04-13).

Scale MMLU Stderr Base Repo
Outlier-10B V3.3 70.87% ±0.37% Qwen2.5-7B-Instruct Outlier-Ai/Outlier-10B
Outlier-40B V3.3 77.80% ±0.33% Qwen2.5-14B-Instruct Outlier-Ai/Outlier-40B
Outlier-70B V3.3 (alpha-fixed) 83.10% ±0.30% Qwen2.5-32B-Instruct Outlier-Ai/Outlier-70B-V3.3
Outlier-150B V3.2 84.46% ±0.29% Qwen2.5-72B-Instruct Outlier-Ai/Outlier-150B-V3.2

Architecture: shared full-precision FFN plus gated ternary expert FFN per layer. Overlay checkpoints load on top of frozen Qwen2.5 bases. 70B V3.3 alpha-fix overlay is 15 KB, trained in 18 minutes on one B200, +1.61pp MMLU over V3.2.


Shipping tier for Apple Silicon

MLX 4-bit builds, verified on Mac Studio M1 Ultra 64GB. Bundled in the Outlier desktop app tier library.

Tier Base Peak RAM Speed Repo
Nano Qwen3-1.7B ~2 GB bench pending Outlier-Nano-1.7B-MLX-4bit
Lite Qwen2.5-7B 4.47 GB 71.30 tok/s Outlier-Lite-7B-MLX-4bit
Compact Qwen2.5-14B 8.24 GB 37.26 tok/s Outlier-Compact-14B-MLX-4bit

Plus cross-platform GGUF builds for llama.cpp / Ollama / LM Studio / Jan: Lite 7B, Compact 14B, Max 32B.


Apple Silicon conversions

MLX 4-bit conversions of strong open-weights models, Mac-tuned, upstream-named for HF search discovery:

Every conversion is a faithful port of upstream weights — capability credit belongs to the upstream authors. We add the MLX 4-bit packaging and desktop-app integration.


Collections


Patents + citation

Architecture, training pipeline, and inference engine covered by US provisional patents 64/026,886, 64/030,368, and 64/034,028 (Kerr & Company LLC, 2026).

@misc{kerr2026outlier,
  title   = {Outlier: Ternary Mixture-of-Experts for On-Device AI},
  author  = {Kerr, Matthew},
  year    = {2026},
  url     = {https://huggingface.co/Outlier-Ai}
}

Contact

Matt Kerr · outlier.host · @mattkerr09 · matt@outlier.host

Built solo in Grand Rapids, Michigan.

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