# houmo-ai/llama.cpp SLOC report

> As of 2026-10-10 (commit df852f59f722), houmo-ai/llama.cpp contains 605,093 total lines: 464,090 code, 47,853 comments, 93,150 blank, across 2,005 files in 38 languages (top: C++ 44.5%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

houmo-ai/llama.cpp has 464,090 source lines of code out of 605,093 total lines across 2,005 files, counted from the postmo ref at commit df852f59f722 by the OctoCounts tokei engine on 2026-10-10. The report is cached by commit, tokei version, and analysis options, so recounting the same revision returns exactly these numbers.

OctoCounts produced this report by resolving houmo-ai/llama.cpp to commit df852f59f722, downloading the repository source archive, and counting every source file with tokei, the open-source line counter written in Rust. The table below breaks the count down by programming language into files, total lines, code lines, comment lines, and blank lines, so the figures can be compared across languages and projects. Results are cached by commit, tokei version, and analysis options, so counting the same revision again reproduces exactly these numbers.

## Repository size insights

This is a large codebase by counted code lines. Code represents 76.7% of all lines, comments represent 7.9%, and the repository averages 231 code lines per file. C++ accounts for 44.5% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| C++ | 401 | 260,725 | 206,642 | 15,864 | 38,219 |
| C | 31 | 46,673 | 36,613 | 1,974 | 8,086 |
| Python | 71 | 44,486 | 34,594 | 2,867 | 7,025 |
| C Header | 192 | 46,918 | 30,639 | 9,214 | 7,065 |
| HTML | 10 | 35,301 | 25,618 | 0 | 9,683 |
| CUDA | 163 | 20,236 | 16,293 | 752 | 3,191 |
| GLSL | 178 | 18,057 | 14,362 | 550 | 3,145 |
| TypeScript | 174 | 22,421 | 14,107 | 5,769 | 2,545 |
| JSON | 14 | 14,066 | 14,055 | 0 | 11 |
| OpenCL | 95 | 14,972 | 12,766 | 0 | 2,206 |
| C++ Header | 49 | 14,264 | 10,917 | 1,227 | 2,120 |
| Svelte | 227 | 8,778 | 7,864 | 47 | 867 |

Top language (C++ 44.5%). Generated at 2026-10-10T06:29:19.548710126+00:00. Canonical report: https://octocounts.com/github/houmo-ai/llama.cpp

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/houmo-ai/llama.cpp/commit/df852f59f72293441b7f8ed5d464cd6cc9b07656?analysis=%7B%22ignoredDirs%22%3A%5B%5D%2C%22ignoredLanguages%22%3A%5B%5D%2C%22profile%22%3A%22default%22%2C%22includeDocs%22%3Afalse%2C%22includeTests%22%3Afalse%2C%22includeGenerated%22%3Afalse%7D&reportId=6a5669addf6184d268d07b5677a2229c39bc124d1636eb32e302de79e3d74ff7).

## Report FAQ

### How many lines of code does houmo-ai/llama.cpp have?

houmo-ai/llama.cpp has 605,093 total lines, including 464,090 code lines, 47,853 comment lines, and 93,150 blank lines.

### How was the houmo-ai/llama.cpp line count measured?

OctoCounts resolved the public GitHub repository to commit df852f59f722, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for houmo-ai/llama.cpp?

This OctoCounts report was generated from postmo at commit df852f59f722 on 2026-10-10T06:29:19.548710126+00:00.

## Similar repository reports

- [Gerate-Technik/zkgram](https://octocounts.com/github/Gerate-Technik/zkgram) — C++, 495,342 code lines
- [harry7557558/spirula-studio](https://octocounts.com/github/harry7557558/spirula-studio) — C++, 498,176 code lines
- [ggml-org/ggml](https://octocounts.com/github/ggml-org/ggml) — C++, 425,101 code lines
- [pointcloudlibrary/pcl](https://octocounts.com/github/pointcloudlibrary/pcl) — C++, 515,670 code lines
- [PointCloudLibrary/pcl](https://octocounts.com/github/PointCloudLibrary/pcl) — C++, 515,729 code lines
- [xinkyle/Voconly](https://octocounts.com/github/xinkyle/Voconly) — C++, 414,062 code lines

## Related OctoCounts pages

- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [TensorFlow vs PyTorch](https://octocounts.com/compare/tensorflow-vs-pytorch)
- [Electron vs Tauri](https://octocounts.com/compare/electron-vs-tauri)
- [Godot vs Bevy](https://octocounts.com/compare/godot-vs-bevy)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [OctoCounts API docs](https://octocounts.com/docs/api)