---
title: AI-CUDA-Engineer-Archive
canonical_url: "https://www.modelscope.cn/datasets/SakanaAI/AI-CUDA-Engineer-Archive"
md_url: "https://www.modelscope.cn/datasets/SakanaAI/AI-CUDA-Engineer-Archive.md"
repository: SakanaAI/AI-CUDA-Engineer-Archive
last_updated: 2025-02-20
license: "Apache License 2.0"
storage_size: "65 MB"
downloads: 1338
stars: 0
---

# AI-CUDA-Engineer-Archive

> AI-CUDA-Engineer-Archive - SakanaAI 在 ModelScope 开源的数据集。The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition

SakanaAI/AI-CUDA-Engineer-Archive 是 ModelScope 魔搭社区上的数据集，存储大小 65 MB，采用 Apache License 2.0 许可。

- **Repository**: SakanaAI/AI-CUDA-Engineer-Archive
- **License**: Apache License 2.0
- **Storage size**: 65 MB
- **Downloads**: 1338
- **Stars**: 0
- **Last updated**: 2025-02-20

Source: https://www.modelscope.cn/datasets/SakanaAI/AI-CUDA-Engineer-Archive

---

# The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition

![](db_scan_kernels.png)

We release [The AI CUDA Engineer archive](https://sakana.ai/ai-cuda-engineer/), a dataset consisting of approximately 30,000 CUDA kernels generated by [The AI CUDA Engineer](https://pub.sakana.ai/ai-cuda-engineer/paper). It is released under the CC-By-4.0 license and can be accessed via HuggingFace and interactively visualized [here](https://pub.sakana.ai/ai-cuda-engineer). The dataset is based on the Kernel tasks provided in [KernelBench](https://scalingintelligence.stanford.edu/KernelBenchLeaderboard/) and includes a torch reference implementation, torch, NCU and Clang-tidy profiling data, multiple kernels per task, error messages and speedup scores against torch native and compile runtimes.

We envision that this dataset can enable post-training of open-source models to perform better CUDA-enabling modules. This includes offline Reinforcement Learning, preference optimization, and standard supervised fine-tuning.

![](archive_table.png)

# Using the AI CUDA Engineer Archive 🕵️

```python
from datasets import load_dataset

dataset = load_dataset("SakanaAI/AI-CUDA-Engineer-Archive")

df_l1 = dataset["level_1"].to_pandas()
print(df_l1.columns)
# Index(['Op_Name', 'Level_ID', 'Task_ID', 'Kernel_Name', 'CUDA_Runtime',
#        'PyTorch_Native_Runtime', 'PyTorch_Compile_Runtime',
#        'CUDA_Speedup_Native', 'CUDA_Speedup_Compile', 'CUDA_Code',
#        'PyTorch_Code_Module', 'PyTorch_Code_Functional', 'Correct', 'Max_Diff',
#        'Error', 'NCU_Profile', 'Torch_Profile', 'Clang_Tidy',
#        '__index_level_0__'],
#       dtype='object')

df_l2 = dataset["level_2"].to_pandas()
df_l3 = dataset["level_3"].to_pandas()
print(df_l1.shape, df_l2.shape, df_l3.shape)
# (12157, 19) (12938, 19) (5520, 19)

# Group by correct and count
print(df_l1["Correct"].value_counts())
print(df_l2["Correct"].value_counts())
print(df_l3["Correct"].value_counts())
```

# Citing The AI CUDA Engineer Archive 👷

If you use **The AI CUDA Engineer** kernel archive in your research or engineering, please cite it as follows:

```
@article{lange2025aicudaengineer,
  title={The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition},
  author={Lange, Robert Tjarko and Prasad, Aaditya and Sun, Qi and Faldor, Maxence and Tang, Yujin and Ha, David},
  journal={arXiv preprint},
  year={2025}
}
```