Comparison
yalm vs whisper.cpp
Verdict
Pick yalm if 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; pick whisper.cpp if a port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription.
Markdown twin · yalm alternatives · whisper.cpp alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | yalm | whisper.cpp |
|---|---|---|
| Maintenance | Slowing (301d since push) As of 6d · github_public_v1 | Very active (0d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 6d · github_public_v1 | Not a fork · Organization account As of 5d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 6d · osv@v1 | No lockfile (source not queried) As of 5d · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- yalm
- LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
- whisper.cpp
- Port of OpenAI's Whisper model in C/C++ for speech-to-text inference
Stars
- yalm
- 591
- whisper.cpp
- 52k
Forks
- yalm
- 64
- whisper.cpp
- 5.9k
Open issues
- yalm
- 4
- whisper.cpp
- 1.2k
Language
- yalm
- C++
- whisper.cpp
- C++
Adopt for
- yalm
- 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.
- whisper.cpp
- A port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription.
Persona
- yalm
- -
- whisper.cpp
- -
Runtime
- yalm
- -
- whisper.cpp
- -
License
- yalm
- -
- whisper.cpp
- MIT License - permissive license that allows users to use the software in any way, including closed-source applications.
Last pushed
- yalm
- Sep 13, 2025
- whisper.cpp
- Jul 11, 2026
Categories
- yalm
- Inference & Serving
- whisper.cpp
- Inference & Serving, Speech & Audio
Trust and health
Maintenance
- yalm
- Slowing (36%)
- whisper.cpp
- Very active (96%)
Days since push
- yalm
- 301d
- whisper.cpp
- 0d
Open issues (now)
- yalm
- 4
- whisper.cpp
- 1.2k
Owner type
- yalm
- User
- whisper.cpp
- Organization
Full report
- yalm
- Trust report
- whisper.cpp
- Trust report
Typed relationship
yalm alternative whisper.cppYALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++.
Choose yalm if…
- YALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++.
- Tags unique to yalm: cpp, cuda, llm-inference, machine-learning.
- When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies
When NOT to use yalm
- 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
Choose whisper.cpp if…
- Requirements: Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by `ggml`..
- YALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++.
- Tags unique to whisper.cpp: inference, openai, speech-recognition, speech-to-text.
- Also covers Speech & Audio.
- You need a lightweight solution that does not require Python or PyTorch
When NOT to use whisper.cpp
- When you prefer to work with higher-level languages like Python which might offer more ease-of-use and extensive libraries
- If your project requires real-time speech transcription and has limited computational resources as `ggml` optimization might still require significant CPU/GPU power for high-performance
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (andrewkchan/yalm) · observed Jul 11, 2026
- GitHub forks (andrewkchan/yalm) · observed Jul 11, 2026
- Last push (andrewkchan/yalm) · observed Sep 13, 2025
- License file (unknown) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ggml-org/whisper.cpp) · observed Jul 11, 2026
- GitHub forks (ggml-org/whisper.cpp) · observed Jul 11, 2026
- Last push (ggml-org/whisper.cpp) · observed Jul 11, 2026
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: yalm 591 · whisper.cpp 52k (synced Jul 11, 2026).
Common questions
- What is the difference between yalm and whisper.cpp?
- yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. whisper.cpp: Port of OpenAI's Whisper model in C/C++ for speech-to-text inference. See the comparison table for live GitHub stats and shared categories.
- When should I choose yalm over whisper.cpp?
- Choose yalm over whisper.cpp when YALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies.
- When should I choose whisper.cpp over yalm?
- Choose whisper.cpp over yalm when Requirements: Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by
ggml.; YALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++; Tags unique to whisper.cpp: inference, openai, speech-recognition, speech-to-text; Also covers Speech & Audio; You need a lightweight solution that does not require Python or PyTorch. - When should I avoid yalm?
- 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
- When should I avoid whisper.cpp?
- When you prefer to work with higher-level languages like Python which might offer more ease-of-use and extensive libraries If your project requires real-time speech transcription and has limited computational resources as
ggmloptimization might still require significant CPU/GPU power for high-performance - Is yalm or whisper.cpp more popular on GitHub?
- whisper.cpp has more GitHub stars (51,715 vs 591). Stars measure visibility, not whether either tool fits your constraints.
- Are yalm and whisper.cpp open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to yalm or whisper.cpp?
- GraphCanon lists graph-backed alternatives at yalm alternatives and whisper.cpp alternatives (yalm markdown twin, whisper.cpp markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, yalm or whisper.cpp?
- yalm: Slowing. whisper.cpp: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for yalm and whisper.cpp?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: yalm trust report; whisper.cpp trust report.