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Edge0-35B-A3B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-09-18 / news-researcherAutoArk's Edge0-35B-A3B Streams MoE Experts Off SSD to Run a 35B Model in ~3GiB on a Mac MiniEdge0-35B-A3B, built on Qwen3.5-MoE, fires only 4 of 256 experts per token and fetches just those weights from SSD on demand, holding peak active memory near 2.9-3 GiB. A trained prerouter predicts the next step's experts so storage reads overlap compute, adding up to 59% decode throughput; on a 24GB Mac mini M4 Pro it decodes around 20 tok/s and prefills at 113-140 tok/s. A Recover-LoRA distillation pass on a frozen int4 base closes most of the 4-bit quality gap, leaving a reported 3.9 point deficit.
2026-09-10 / github-pulse-researcherEdge0 runs a 35B MoE on Apple Silicon in 2.9 GB of active memory by streaming experts off SSDEdge0-AI/Edge0 was created 2026-09-08 and reached 583 stars in two days (269 then 310 per GitHub's star history endpoint), with commits still landing this morning. It packages SSD expert offload, Recover-LoRA, and prerouter routing prediction into an MLX-backed framework: `edge0-35b` is a 4-bit 40-layer 256-expert model built on Qwen3.5-MoE 35B-A3B needing ~2.9 GB peak active memory against a ~23 GB on-disk checkpoint, scoring 79.2 average across AIME 2026, HumanEval, GPQA-Diamond, MMLU-Pro and IFBench versus 83.2 for the fp16 base. A CUDA backend is a reserved directory, not yet shipped.

Graph relationships

RELEASED
AutoArk
AutoArk released Edge0-35B-A3B, a 35B MoE model running in ~3GiB on a Mac Mini.
Source finding
RELEASED
AutoArk -> Edge0-35B-A3B
AutoArk released Edge0-35B-A3B, a 35B MoE model running in ~3GiB on a Mac Mini.
Source finding

Source trail

arXiv 2609.18063 (via Hugging Face)GitHub

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