- Updated: February 17, 2026
- 5 min read
MicroGPT‑C: Ultra‑Low‑Latency LLM in Pure C – A New Milestone for AI on Edge
MicroGPT‑C is a zero‑dependency, pure‑C implementation of a GPT‑style language model that delivers ultra‑low‑latency AI inference and training, making it ideal for developers, researchers, and edge‑device engineers who need an open‑source LLM that runs in milliseconds.
MicroGPT‑C: A Fast, Open‑Source LLM for AI Development
Released under the permissive MIT license, MicroGPT‑C replicates the core architecture of Andrej Karpathy’s microgpt.py but compiles to native C code with optional SIMD auto‑vectorisation. The result is a model that can be trained in tens of milliseconds and generate text in microseconds, all without Python, PyTorch, or a GPU.
Project Overview
Designed for education, rapid prototyping, and constrained‑device deployment, MicroGPT‑C targets the following audiences:
- AI developers seeking a lightweight baseline for open‑source LLM experimentation.
- Machine‑learning researchers who need a fully auditable implementation for quantisation or custom optimiser studies.
- Edge‑engineers and IoT hobbyists wanting a model that fits into <50 KB of RAM.
For teams already using the UBOS platform overview, MicroGPT‑C can be integrated as a microservice within the Workflow automation studio, enabling AI‑driven pipelines without leaving the UBOS ecosystem.
Key Features & Architecture
The implementation follows a single‑layer decoder‑only Transformer, mirroring GPT‑2’s design but stripped to the essentials:
Core Features
- Zero external dependencies – pure C99.
- Optional SIMD auto‑vectorisation for 4,600× training speed‑up.
- INT8 quantisation reduces model size by 90 % (≈4.6 KB).
- Single‑file build via
microgpt_amalgamated.cfor rapid prototyping. - Compatible with standard C compilers (GCC, Clang, MSVC) and CMake.
Architecture Snapshot
Input → Token Embed + Pos Embed → RMSNorm → Self‑Attention (4 heads) → Residual
→ RMSNorm → MLP (fc1 → ReLU → fc2) → Residual → Linear (lm_head) → Softmax
The model uses a 16‑dimensional embedding, four attention heads, and a context length of 16 tokens, resulting in roughly 4,600 parameters (≈37 KB in FP64, 4.6 KB in INT8).
Below is a visual representation of the architecture (generated image):
Performance Benchmarks
MicroGPT‑C’s speed advantage is evident when compared to the reference Python implementation on identical workloads (1,000 training steps, 20 inference samples):
| Metric | Python Reference | MicroGPT‑C (FP64) | Speed‑up |
|---|---|---|---|
| Training time | 93 s | 0.02 s | ≈4,600× |
| Training throughput | 0.1 k tok/s | 289 k tok/s | ≈2,800× |
| Inference time (single sample) | 0.74 s | <1 ms | ≈700× |
| Inference rate | 27 samples/s | 20,000 samples/s | ≈740× |
Even the INT8‑quantised build only incurs a modest 25 % slowdown while shrinking the weight footprint dramatically—perfect for UBOS solutions for SMBs that run on low‑cost hardware.
Getting Started: Installation & First Run
MicroGPT‑C can be compiled on Linux, macOS, or Windows with a single command. Follow these steps:
- Clone the repository:
git clone https://github.com/enjector/microgpt-c.git && cd microgpt-c - Choose your build mode:
- Standard build (scalar):
./build.sh - SIMD‑enabled build for maximum speed:
./build.sh --simd - INT8 quantised build:
./build_quantised.sh --simd
- Standard build (scalar):
- Run the executable:
./build/microgptThe script automatically copies
names.txt(training data) next to the binary. - Generate text:
./build/microgpt --sample 10You’ll see realistic names appear within milliseconds.
For Windows users, the equivalent batch files (build.bat and build_quantised.bat) provide the same functionality. Detailed instructions are also available in the AI solutions section of UBOS, where you can spin up a containerised version of MicroGPT‑C with a single click.
Community, Contributions & Ecosystem
MicroGPT‑C thrives on an open‑source community that values transparency and reproducibility. Here’s how you can get involved:
- Issue tracking: Report bugs or request features directly on the GitHub issues page.
- Pull requests: Contribute enhancements such as additional quantisation schemes, custom tokenisers, or integration hooks.
- Discussion forums: Join the UBOS blog for tutorials on embedding MicroGPT‑C into larger AI pipelines.
- Template marketplace: Leverage ready‑made UBOS templates like the AI SEO Analyzer or AI Chatbot template to prototype services that call MicroGPT‑C via a lightweight REST wrapper.
Because the codebase is deliberately minimal (under 2 KB of core logic), it serves as an excellent teaching tool for university courses on About UBOS and deep‑learning fundamentals.
Real‑World Use Cases
Below are three practical scenarios where MicroGPT‑C shines:
Edge Device Text Generation
Deploy the Enterprise AI platform by UBOS on a Raspberry Pi and run MicroGPT‑C to generate on‑device prompts for voice assistants, eliminating cloud latency.
Microservice for Rapid Prototyping
Wrap the binary in a Docker container and expose a simple HTTP endpoint. Combine it with the AI marketing agents to produce personalized copy in real time.
Research on Quantisation
Use the INT8 build as a baseline for academic papers on model compression, comparing token‑level accuracy against the FP64 reference.
Conclusion & Next Steps
MicroGPT‑C proves that high‑performance language modeling does not require heavyweight frameworks. Its minimal footprint, SIMD acceleration, and optional quantisation make it a versatile tool for AI developers, machine‑learning researchers, and hobbyists alike.
Ready to experiment? Clone the repository on GitHub today, and explore how this tiny transformer can power your next AI‑driven product. For a seamless integration path, consider pairing MicroGPT‑C with UBOS’s Web app editor on UBOS or the UBOS partner program to accelerate deployment.
Stay updated with the latest releases, community showcases, and advanced tutorials by visiting the UBOS pricing plans page for the tier that matches your project scale.
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