# gemma.cpp — Lightweight C++ Inference Engine for Google Gemma Models

> A standalone, lightweight C++ inference engine from Google for running Gemma language models locally with minimal dependencies, designed for research experimentation and embedded deployment.

## Install

Save in your project root:

# gemma.cpp — Lightweight C++ Inference Engine for Google Gemma Models

## Quick Use
```bash
git clone https://github.com/google/gemma.cpp.git
cd gemma.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --target gemma -j$(nproc)
# Run interactive chat
./build/gemma --tokenizer tokenizer.spm --model 2b-it --weights gemma-2b-it.sbs
```

## Introduction
gemma.cpp is Google's official lightweight C++ implementation for running Gemma language models. It prioritizes simplicity and minimal dependencies over maximum throughput, making it a research-friendly alternative to larger inference frameworks. The single-file design and CPU-focused approach enable experimentation on laptops without GPU requirements.

## What gemma.cpp Does
- Runs Gemma 2B and 7B models in pure C++ with no external ML framework dependencies
- Provides interactive text generation with streaming token output
- Supports both instruction-tuned (IT) and base pretrained model variants
- Implements core transformer operations using the Highway library for SIMD vectorization
- Enables batch processing and embedding extraction for research workflows

## Architecture Overview
The engine implements the Gemma transformer architecture (multi-query attention, GeGLU activations, RMSNorm) in C++ using Google's Highway library for portable SIMD operations. It loads model weights from a custom compressed format (.sbs), performs inference on CPU using matrix multiplication kernels optimized for the host architecture (SSE, AVX, NEON), and generates tokens autoregressively with configurable sampling parameters.

## Self-Hosting & Configuration
- Build with CMake on Linux, macOS, or Windows with a C++17 compiler
- Download model weights from Kaggle in the supported .sbs format
- Configure generation parameters (temperature, top-k, top-p) via command-line flags
- Link as a library in custom C++ applications for embedded inference
- Optionally enable OpenMP for multi-threaded matrix operations

## Key Features
- Minimal dependencies: only requires a C++17 compiler and CMake to build
- Portable across CPU architectures with SIMD optimization via Highway
- Small binary footprint suitable for embedded and edge deployment
- Research-friendly single-codebase design for understanding transformer internals
- Supports both Gemma 1 and Gemma 2 model architectures

## Comparison with Similar Tools
- **llama.cpp** — broader model support with GGUF format; gemma.cpp is Gemma-specific and Google-maintained
- **ONNX Runtime** — general-purpose with GPU support; gemma.cpp is CPU-only and much lighter
- **Hugging Face Transformers** — Python with full ecosystem; gemma.cpp is pure C++ with no Python
- **MediaPipe LLM** — Google's mobile inference; gemma.cpp targets desktop and research use
- **MLC-LLM** — compiler-based with broad hardware; gemma.cpp is simpler with manual optimization

## FAQ
**Q: Why use gemma.cpp instead of llama.cpp for Gemma models?**
A: gemma.cpp is Google's reference implementation with guaranteed correctness for Gemma architectures. llama.cpp offers broader model support and GPU acceleration.

**Q: Does it support GPU acceleration?**
A: Currently it is CPU-only, focusing on portability and simplicity. For GPU inference, use Gemma via Hugging Face or vLLM.

**Q: What are the hardware requirements?**
A: The 2B model runs comfortably on a laptop with 8GB RAM. The 7B model needs approximately 16GB of available memory.

**Q: Can I use it for fine-tuned Gemma models?**
A: It supports loading custom weights in the .sbs format. You would need to convert fine-tuned weights from other formats.

## Sources
- https://github.com/google/gemma.cpp
- https://ai.google.dev/gemma

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Author: AI Open Source