LeapQuant
LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization
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GITHUB · launched Nov 11, 2026 · 50 upvotes (top 63%) · 0 views
Niche: machine learning inference engines · 108 launches, 8 in the last 7 days ▼ -27% · Explore this niche →
Category and tags
- Horizontal
- Model & infra
- Developer
- AI-native
- Hobby / open-source project
Visit website ↗ Source listing ↗
More like this: all 108 launches in machine learning inference engines →
Could you build this?
No This is GPU kernel research on quantizing recurrent state in linear attention models, requiring CUDA and inference-systems expertise.
What it would actually take: A real version needs custom CUDA or Triton kernels for fused state quantization and dequantization, plus integration into a serving engine such as vLLM or SGLang. Reproducing the accuracy results requires understanding of linear attention architectures (GDN, KDA), error analysis, and access to B200 or RTX-class GPUs for benchmarking.
Competitors
Other products that read as similar to this one : 2090 launches clear the similarity bar, closest 8 shown.
Attention rank: #1311 of 2091 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 377 days after the earliest competitor.
- STEPQuant · github · 2026-09-29 · 57 upvotes · similarity 0.72
- fp4Attn · github · 2026-10-25 · 15 upvotes · similarity 0.70
- DASA · github · 2026-10-13 · 29 upvotes · similarity 0.63
- FlashMask · github · 2026-10-14 · 35 upvotes · similarity 0.62
- DeepSelect · github · 2026-09-09 · 327 upvotes · similarity 0.62
- flat · github · 2026-10-12 · 19 upvotes · similarity 0.60
- KronLSTM · github · 2026-10-19 · 49 upvotes · similarity 0.60
- TRACE · github · 2026-10-03 · 10 upvotes · similarity 0.60
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.