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maple

MAPLE: Manifold-Activated Pruning of Latent Experts for Sub-2-BPW MoE LLMs

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GITHUB · launched Nov 7, 2026 · 47 upvotes (top 65%) · 0 views

Niche: specialized algorithms and computing libraries · 230 launches, 4 in the last 7 days ▼ -73% · Explore this niche →

Category and tags

  • Horizontal
  • Model & infra
  • Developer
  • AI-native
  • Hobby / open-source project

Visit website ↗ Source listing ↗

Could you build this?

No The project is novel ML research on pruning and quantizing mixture-of-experts LLMs to under 2 bits per weight, which requires research-level expertise and substantial GPU compute.

What it would actually take: Reproducing this means studying MoE routing and expert activation statistics, designing a manifold-based pruning criterion, and developing low-bit quantization kernels and evaluation pipelines. It requires ML researchers, GPU kernel engineers, and access to large models and multi-GPU hardware for calibration and benchmarking.

Competitors

Other products that read as similar to this one : 3032 launches clear the similarity bar, closest 8 shown.

Attention rank: #1898 of 3033 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 374 days after the earliest competitor.

The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.