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Maple

Sub-2-BPW Moe Pruning to Run Gemma 4 26B at 45 TPS on a 6GB GPU

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HN · launched Nov 8, 2026 · 21 upvotes (top 27%) · 0 views

Niche: llm inference and deployment tools · 188 launches, 6 in the last 7 days · 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 product is a research-level model compression technique (sub-2-bit-per-weight MoE pruning) that runs a large model on small hardware, which requires ML research expertise that vibe coding cannot supply.

What it would actually take: Building a comparable version means implementing and validating novel quantization and expert-pruning methods on the target model, plus heavy GPU experimentation to measure quality against throughput. It requires ML engineers who understand quantization kernels, MoE routing, and CUDA performance tuning. The website text is mostly scraped GitHub page boilerplate, so the exact method is not verifiable from what was provided.

Discussion

5 comments analyzed.

Concerns raised: performance impact unclear, only tested on trivial tasks, Gemma is MoE so expert balancing may not hold, skeptical of expectations for this model

Feature requests: Mac tokens-per-second benchmarks, known coding benchmark comparison against original model, apply the process to LFM2.5-24B-A2B

Competitors

Other products that read as similar to this one : 758 launches clear the similarity bar, closest 8 shown.

Attention rank: #203 of 759 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 366 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.