yalm
LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
GraphCanon updated 4w · GitHub synced 4w
Decision brief
YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries.
Good fit when
- When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies
- In scenarios where performance optimization is paramount and you require a lightweight solution that directly leverages hardware capabilities via CUDA
Avoid when
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs
- For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (315d since push)
- As of 4w
- Provenance
- Not a fork · Personal account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/andrewkchan/yalmHow it fits your stack(1)
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Overview
A lightweight LLM inference solution implemented in C++ and CUDA without the need for additional machine learning libraries, focusing solely on LLM inference tasks.
Capability facts
- Languages
- c++
Source: github.language · Jul 25, 2026
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README
install git LFS
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get -y install git-lfs
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