纯C高性能GPT
MicroGPT-C是一个完全用C实现的GPT模型，在Apple M5上达到1000万每秒的事务处理速度，并提供比Python版本快2500倍的性能。 该项目因其高性能而具有重要意义，在Apple M5上实现了1000万每秒的事务处理速度，比Python快2500倍，并获得了积极的社区参与，评分125分，评论43条，表明对性能关键应用有浓厚兴趣。 该项目采用宽松的许可证，处于生产就绪的成熟度，部署
项目链接：https://github.com/vixhal-baraiya/microgpt-c
作者：dhorthy
发布时间：2026-08-18T15:46:46Z
挖掘日期：2026-08-20
AI 评分：9.0/10
来源：hackernews
标签：LLM, C, Performance, GPT, LanguageModel
📌 项目详解
MicroGPT-C是一个完全用C实现的GPT模型，在Apple M5上达到1000万每秒的事务处理速度，并提供比Python版本快2500倍的性能。 该项目因其高性能而具有重要意义，在Apple M5上实现了1000万每秒的事务处理速度，比Python快2500倍，并获得了积极的社区参与，评分125分，评论43条，表明对性能关键应用有浓厚兴趣。 该项目采用宽松的许可证，处于生产就绪的成熟度，部署复杂度低，硬件资源需求 minimal，适合集成到各种系统中。
🌐 背景与生态
该项目位于高性能语言模型的生态系统中，填补了希望使用基于C的实现以提高速度和效率的领域。最近的编译器优化和硬件加速进展使得这种纯C实现成为可能。
💬 社区讨论
社区评论对速度提升表示兴奋，要求进一步优化，并讨论了在现实场景中的潜在应用。
🚀 应用前景
MicroGPT-C可应用于需要高速语言模型推理的场景，如实时聊天机器人、嵌入式系统中的自然语言处理以及性能敏感的企业应用。
🔧 技术栈
核心技术栈包括纯C，无外部依赖，利用编译器优化和 minimal 硬件资源。
🎯 上手难度
入门难度评级为入门。前提条件包括C编译器和对GPT架构的基本理解。使用gcc编译并运行提供的示例脚本。
👥 目标用户
目标用户包括后端工程师、嵌入式系统开发人员和对高性能语言模型感兴趣的研究人员。
⚖️ 类似项目对比
竞品项目包括gpt2.c和llm.c，它们也是纯C实现的GPT模型，提供类似的性能优势，但优化和硬件利用的侧重点不同。
📚 参考链接
📄 查看原文内容
--- Top Comments ---
[MycroftJones]: Check out this port of microgpt to C, posted 5 months ago. It got a 2500x speedup over the python version. https://github.com/moebiusV/cugpt
[Retr0id]: > The most atomic way to train and inference a GPT in pure, dependency-free C. What sense of the word "atomic" is meant here?
[hasteg]: This is pretty cool. Implemented something similar myself (a really small language model with ~10M params) just to teach myself the ML behind the LLMs. Did not implement it in C obviously, just use PyTorch, but it's interesting to go through the c file and see how he has implemented stuff I took for granted in Python in C. Anyway, I just tested this out myself on my AMD Ryzen 9 9800x3d. I got 7647173 tok/sec using karpathy's Shakespeare dataset https://raw.githubuser...
[ilaksh]: This is not an LLM obviously
, it's just for generating random names. But interesting to think of the possibilities of truly tiny language models if there were connected together.
[pkilgore]: Honestly not sure this is impressive. I ported microgpt to zig as a learning exercise, then moved scalar engines to NEON/metal just to see what happened. Besides metal being slower (I probably did something wrong, but it could be due to the fixed costs of memory transfer into the GPU not being worth it due to the small model). Anyways, it was also stupid fast, particularly compared to the python version. But I was pretty sure that's irrelevant to real production architectures!