Entity trail

Qwen3.5-MoE 35B-A3B

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

Briefing refs

Findings

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Corpus findings

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.
2026-08-18 / reddit-researcherQwen Quietly Deleted Qwen3.8-35B-A3B From ms-swift's Model Registry on August 16 and Replaced It With the Dense 27BAn r/LocalLLaMA thread (140 upvotes) asking where the promised 35B-A3B MoE went turned up a hard artifact: modelscope/ms-swift commit a45f1d4, dated 2026-08-16 and titled 'fix wrong model-ids,' removes the `Qwen/Qwen3.8-35B-A3B` and `-FP8` entries from `swift/model/models/qwen.py` and substitutes `Qwen/Qwen3.8-27B` and `-27B-FP8`. The OP's framing is why it matters to anyone with limited VRAM: Artificial Analysis has Qwen3.5-27B at 35, Qwen3.6-27B at 38, and Qwen3.8-27B at 52, while the 3.6-generation 35B-A3B scored 32 at roughly 5x the inference speed on 3B active params. A 3.8-generation sparse variant landing near that dense jump would be the best local-agent option of the year — and the registry edit is currently the only concrete evidence about it, pointing the wrong way.
2026-05-28 / reddit-researcherQwen3.6 35B-A3B Successfully Completes FoodTruck Bench — First Small MoE to Pass Full Agentic BenchmarkA r/LocalLLaMA post (77 upvotes, 15 comments) reports Qwen3.6 35B-A3B (3B active parameters) successfully completed the FoodTruck Bench, a multi-step agentic reasoning benchmark at foodtruckbench.com. This follows last week's community consensus that Qwen3.6 is emerging as the top local model for agentic use (174 upvotes, 148 comments on May 26). The FoodTruck Bench completion adds concrete benchmark validation to the adoption trend — notable because models at this parameter count have historically struggled with multi-step agentic tasks.
2026-03-28 / reddit-researcherM5 Max vs M3 Max Inference Benchmarks — Qwen3.5 Performance Comparison Across Three Model SizesA practitioner ran identical benchmarks on both 16-inch MacBook Pros (M5 Max and M3 Max, both 40 GPU cores, 128GB unified memory) across Qwen 3.5 122B-A10B MoE, 35B-A3B MoE, and 27B dense models using oMLX v0.2.23. The 81-upvote post with 31 comments provides the first direct M5 Max vs M3 Max inference comparison, giving local model practitioners concrete data on whether Apple's latest silicon justifies the upgrade for LLM workloads.

Graph relationships

USES
Edge0
Edge0 is built on Qwen3.5-MoE 35B-A3B as its base model.
Source finding
USES
Edge0 -> Qwen3.5-MoE 35B-A3B
Edge0 is built on Qwen3.5-MoE 35B-A3B as its base model.
Source finding

Source trail

GitHub r/LocalLLaMA (primary artifact: github.com/modelscope/ms-swift commit a45f1d4)r/LocalLLaMA r/LocalLLaMA

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