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UL-SMF

Open-source linear-complexity ~300x KV-cache compression

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HN · launched Aug 17, 2026 · 13 upvotes (top 34%) · 0 views

Niche: llm inference and deployment tools · 188 launches, 7 in the last 7 days ▲ +17% · 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 Implementing ~300x KV-cache compression with linear complexity requires novel deep learning research, custom CUDA/Triton kernels, and mathematical expertise in matrix factorization and attention mechanisms.

What it would actually take: Requires writing custom GPU kernels (CUDA/C++ or Triton) integrated into an inference engine like vLLM or Hugging Face Transformers. The engineering demands deep theoretical knowledge of attention KV-caches, structured matrix factorization (SMF), and low-level GPU memory bandwidth optimization to achieve compression without unacceptable perplexity degradation.

Discussion

1 comment analyzed.

Concerns raised: Cosine similarity not suitable for attention compression, Lack of clear demonstration that it works

Competitors

Other products that read as similar to this one : 3151 launches clear the similarity bar, closest 8 shown.

Attention rank: #1019 of 3152 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 290 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.