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sparse-memory-lm

A 21M-parameter LM with a 16.8M-row product-key memory: as good as a 114M dense model and runs with the table on an SSD. Triton kernels for ROCm and CUDA.

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This is 1 of 239 launches in model optimization and inference runtimes — see how it stacks up on momentum and crowding →

727 other launches read as similar to this one →

Details

External ID
1403330296
Source
GITHUB
Company
—
Product
sparse-memory-lm
Website domain
github.com
Launched
Oct. 3, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.09
Tags
amd, language-model, memory-layers, product-key-memory, pytorch, rocm, triton

Enrichment

Niche
model optimization and inference runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
memory-augmented language model for resource-constrained hardware
Manually corrected
False

Could you build this?

No Developing a custom sparse-memory language model with a 16.8M-row product-key memory and custom Triton GPU kernels running from SSD requires advanced deep learning systems research.

What it would actually take: Requires research-level expertise in transformer architectures, Product-Key Memory (PKM), asynchronous I/O (io_uring/SSD paging), and writing high-performance low-level Triton/CUDA/ROCm kernels to handle sparse memory lookups with minimal latency.

Competitors

Other products that read as similar to this one — 727 launches clear the similarity bar, closest 8 shown.

Attention rank: #653 of 728 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 335 days after the earliest competitor.

Other launches for this product