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SHADOW-50M-Instruct

SHADOW 50M Instruct: a proof of concept. 44M ternary parameters, exact circuits inside the model, memory on disk, 19.8 MB, offline on a CPU.

Details

External ID
1371231327
Source
GITHUB
Company
—
Product
SHADOW-50M-Instruct
Website domain
github.com
Launched
Sept. 15, 2026
Cohort
—
Upvotes
29
Upvotes percentile
0.7161798616448886
Tags
—
Fetched at
Sept. 19, 2026, 5:02 p.m.
Updated at
Sept. 19, 2026, 5:02 p.m.

Enrichment

Theme
lightweight and on-device AI runtimes
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
compact ternary parameter language model for offline cpu inference
Manually corrected
False

Could you build this?

No Creating a novel ternary-quantized 50M parameter language model with custom circuit architectures runnable directly from disk on CPU requires advanced deep learning and hardware-level systems research.

What it would actually take: Implementation requires custom deep learning research frameworks in PyTorch/C++/CUDA, custom quantization kernels (e.g., 1.58-bit ternary matrix multiplication like BitNet b1.58), and low-level memory-mapped disk I/O execution engines. Deep expertise in AI architectures, quantization theory, and low-level systems engineering is mandatory.

Competitors

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

Attention rank: #4 of 9 (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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