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UL-SMF

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

Details

External ID
49333084
Source
HN
Company
—
Product
UL-SMF
Website domain
github.com
Launched
Aug. 17, 2026
Cohort
—
Upvotes
13
Upvotes percentile
0.6639784946236559
Tags
—
Fetched at
Sept. 10, 2026, 5:32 a.m.
Updated at
Sept. 10, 2026, 5:32 a.m.

Enrichment

Theme
systems tools and desktop utilities
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
kv-cache compression for language models
Manually corrected
False

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 — 1314 launches clear the similarity bar, closest 8 shown.

Attention rank: #437 of 1315 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 290 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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