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laya-mlx

Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.

Details

External ID
1377211520
Source
GITHUB
Company
—
Product
laya-mlx
Website domain
pypi.org
Launched
Sept. 19, 2026
Cohort
—
Upvotes
5859
Upvotes percentile
0.9993594670766077
Tags
apple-silicon, decision-model, inference, laya, local-ai, machine-learning, mlx, modernbert, system-one, typed-decisions
Fetched at
Sept. 23, 2026, 5:02 p.m.
Updated at
Sept. 23, 2026, 5:02 p.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
mlx runtime for laya decision models on apple silicon
Manually corrected
False

Could you build this?

No Writing a custom, high-performance C++/Metal MLX runtime with sub-15ms inference latency and zero-token classification/decision heads on Apple Silicon requires specialized low-level machine learning systems engineering.

What it would actually take: Requires implementing custom MLX/C++ computational graphs and Apple Metal kernels to evaluate encoder/decision architectures without typical generative autoregressive overhead. Needs deep proficiency in Apple Silicon unified memory architecture, MLX compiler internals, weight quantization, and custom inference runtime optimization to achieve stable single-digit millisecond latency benchmarks.

Competitors

Other products that read as similar to this one — 286 launches clear the similarity bar, closest 8 shown.

Attention rank: #2 of 287 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 320 days after the earliest competitor.

Other launches for this product

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