---
title: "yalm vs whisper.cpp"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/andrewkchan-yalm-vs-ggml-org-whisper-cpp"
tools: ["andrewkchan-yalm", "ggml-org-whisper-cpp"]
---

# yalm vs whisper.cpp

*GraphCanon updated Jul 17, 2026*

## 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.

[yalm](https://github.com/andrewkchan/yalm) reports 591 GitHub stars, 64 forks, and 4 open issues, last pushed Sep 13, 2025. [whisper.cpp](https://github.com/ggml-org/whisper.cpp) has 52k stars, 5.9k forks, and 1.2k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [yalm's repository](https://github.com/andrewkchan/yalm) and [whisper.cpp's repository](https://github.com/ggml-org/whisper.cpp).

| | [yalm](/tools/andrewkchan-yalm.md) | [whisper.cpp](/tools/ggml-org-whisper-cpp.md) |
| --- | --- | --- |
| Tagline | LLM inference engine in C++/CUDA without dependency on external libraries except for I/O | Port of OpenAI's Whisper model in C/C++ for speech-to-text inference |
| Stars | 591 | 51,715 |
| Forks | 64 | 5,898 |
| Open issues | 4 | 1,216 |
| Language | C++ | C++ |
| Adopt for | 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. | A port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT License - permissive license that allows users to use the software in any way, including closed-source applications. |
| Categories | Inference & Serving | Inference & Serving, Speech & Audio |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [yalm](/tools/andrewkchan-yalm.md) | [whisper.cpp](/tools/ggml-org-whisper-cpp.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 301d | 0d |
| Open issues (now) | 4 | 1.2k |
| Owner type | User | Organization |
| Full report | [trust report](/tools/andrewkchan-yalm/trust.md) | [trust report](/tools/ggml-org-whisper-cpp/trust.md) |

**Typed relationship:** yalm _(alternative)_ whisper.cpp

YALM focuses on LLM inference in C++/CUDA, similar to whisper.cpp which also focuses on efficient execution of models with C/C++.

## Decision facts: yalm

- **Adopt for:** 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.

## Decision facts: whisper.cpp

- **Requirements:** Requires C++ setup and knowledge; Needs audio files converted into a compatible format supported by `ggml`.
- **Adopt for:** A port of OpenAI's Whisper model to C++, optimized with `ggml`, for lightweight speech-to-text transcription.
- **License detail:** MIT License - permissive license that allows users to use the software in any way, including closed-source applications.

## Choose when

### 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

### 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 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 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

## 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 `ggml` optimization 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](/tools/andrewkchan-yalm/alternatives) and [whisper.cpp alternatives](/tools/ggml-org-whisper-cpp/alternatives) ([yalm markdown twin](/tools/andrewkchan-yalm/alternatives.md), [whisper.cpp markdown twin](/tools/ggml-org-whisper-cpp/alternatives.md)), 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](/compare/andrewkchan-yalm-vs-ggml-org-whisper-cpp.md) 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](/tools/andrewkchan-yalm/trust); [whisper.cpp trust report](/tools/ggml-org-whisper-cpp/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=andrewkchan-yalm`](/api/graphcanon/graph?tool=andrewkchan-yalm)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_