# deepseek-ai/DeepInfer SLOC report

> As of 2026-10-22 (commit 9bd9cf243fc5), deepseek-ai/DeepInfer contains 59,629 total lines: 49,741 code, 2,750 comments, 7,138 blank, across 309 files in 8 languages (top: Python 89.7%). Custom configuration (tests excluded, docs excluded, generated excluded). Counted with tokei via OctoCounts.

deepseek-ai/DeepInfer has 49,741 source lines of code out of 59,629 total lines across 309 files, counted from the main ref at commit 9bd9cf243fc5 by the OctoCounts tokei engine on 2026-10-22. 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 deepseek-ai/DeepInfer to commit 9bd9cf243fc5, 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 medium-sized codebase by counted code lines. Code represents 83.4% of all lines, comments represent 4.6%, and the repository averages 161 code lines per file. Python accounts for 89.7% of counted code.

| Language | Files | Lines | Code | Comments | Blanks |
| --- | ---: | ---: | ---: | ---: | ---: |
| Python | 255 | 52,631 | 44,608 | 1,891 | 6,132 |
| C++ Header | 18 | 3,484 | 2,716 | 286 | 482 |
| C++ | 15 | 2,425 | 2,036 | 115 | 274 |
| Shell | 10 | 410 | 297 | 49 | 64 |
| TOML | 1 | 82 | 59 | 19 | 4 |
| Makefile | 1 | 32 | 25 | 0 | 7 |
| Markdown | 5 | 516 | 0 | 346 | 170 |
| Plain Text | 4 | 49 | 0 | 44 | 5 |

Top language (Python 89.7%). Generated at 2026-10-22T07:00:25.993790430+00:00. Canonical report: https://octocounts.com/github/deepseek-ai/DeepInfer

## Reproduce this report

[Reproduce this exact report and configuration](https://octocounts.com/github/deepseek-ai/DeepInfer/commit/9bd9cf243fc53ef9afbf8f04d391d989117d6ab9?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=c82844f0a16513a06e0a29fddbec3d24f93579af6d4727440eaab0053c30e687).

## Report FAQ

### How many lines of code does deepseek-ai/DeepInfer have?

deepseek-ai/DeepInfer has 59,629 total lines, including 49,741 code lines, 2,750 comment lines, and 7,138 blank lines.

### How was the deepseek-ai/DeepInfer line count measured?

OctoCounts resolved the public GitHub repository to commit 9bd9cf243fc5, downloaded the source archive, counted it with tokei, and cached the report by commit, tokei version, and analysis options.

### What commit was counted for deepseek-ai/DeepInfer?

This OctoCounts report was generated from main at commit 9bd9cf243fc5 on 2026-10-22T07:00:25.993790430+00:00.

## Similar repository reports

- [bbfamily/abu](https://octocounts.com/github/bbfamily/abu) — Python, 49,805 code lines
- [roboflow/supervision](https://octocounts.com/github/roboflow/supervision) — Python, 50,052 code lines
- [Kruk2/jasna](https://octocounts.com/github/Kruk2/jasna) — Python, 50,422 code lines
- [Lightning-AI/pytorch-lightning](https://octocounts.com/github/Lightning-AI/pytorch-lightning) — Python, 50,467 code lines
- [RDFLib/rdflib](https://octocounts.com/github/RDFLib/rdflib) — Python, 47,846 code lines
- [lulufoo/lulu-workflow](https://octocounts.com/github/lulufoo/lulu-workflow) — Python, 51,773 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)
- [Django vs Ruby on Rails](https://octocounts.com/compare/django-vs-rails)
- [Laravel vs Django](https://octocounts.com/compare/laravel-vs-django)
- [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)