gemma.cpp is a lightweight, standalone C++ inference engine for the Gemma foundation models from Google.

For additional information about Gemma, see ai.google.dev/gemma. Model weights, including gemma.cpp specific artifacts, are available on kaggle.

Modern LLM inference engines are sophisticated systems, often with bespoke capabilities extending beyond traditional neural network runtimes. With this comes opportunities for research and innovation through co-design of high level algorithms and low-level computation. However, there is a gap between deployment-oriented C++ inference runtimes, which are not designed for experimentation, and Python-centric ML research frameworks, which abstract away low-level computation through compilation.

gemma.cpp provides a minimalist implementation of Gemma-2, Gemma-3, and PaliGemma-2 models, focusing on simplicity and directness rather than full generality. This is inspired by vertically-integrated model implementations such as ggml, llama.c, and llama.rs.

gemma.cpp targets experimentation and research use cases. It is intended to be straightforward to embed in other projects with minimal dependencies and also easily modifiable with a small ~2K LoC core implementation (along with ~4K LoC of supporting utilities). We use the Google Highway Library to take advantage of portable SIMD for CPU inference.

For production-oriented edge deployments we recommend standard deployment pathways using Python frameworks like JAX, Keras, PyTorch, and Transformers (all model variations here).

Community contributions large and small are welcome. See DEVELOPERS.md for additional notes contributing developers and join the discord by following this invite link. This project follows Google's Open Source Community Guidelines.

[!NOTE] Active development is currently done on the dev branch. Please open pull requests targeting dev branch instead of main, which is intended to be more stable.

• LLM

  • CPU-only inference for: Gemma 2-3, Griffin(SSM), PaliGemma 2.
  • Sampling with TopK and temperature.
  • Backward pass (VJP) and Adam optimizer for Gemma research.

• Optimizations

  • Mixed-precision (fp8, bf16, fp32, fp64 bit) GEMM:
    • Designed for BF16 instructions, can efficiently emulate them.
    • Automatic runtime autotuning 7 parameters per matrix shape.
  • Weight compression integrated directly into GEMM:
    • Custom fp8 format with 2..3 mantissa bits; tensor scaling.
    • Also bf16, f32 and non-uniform 4-bit (NUQ); easy to add new formats.

• Infrastructure

  • SIMD: single implementation via Highway. Chooses ISA at runtime.
  • Tensor parallelism: CCX-aware, multi-socket thread pool.
  • Disk I/O: memory map or parallel read (heuristic with user override).
  • Custom format with forward/backward-compatible metadata serialization.
  • Model conversion from Safetensors, not yet open sourced.
  • Portability: Linux, Windows/OS X supported. CMake/Bazel. 'Any' CPU.

• Frontends

  • C++ APIs with streaming for single query and batched inference.
  • Basic interactive command-line app.
  • Basic Python bindings (pybind11).

Before starting, you should have installed:

• CMake
• Clang C++ compiler, supporting at least C++17.
• tar for extracting archives from Kaggle.

Building natively on Windows requires the Visual Studio 2012 Build Tools with the optional Clang/LLVM C++ frontend (clang-cl). This can be installed from the command line with winget:

winget install --id Kitware.CMake
winget install --id Microsoft.VisualStudio.2022.BuildTools --force --override "--passive --wait --add Microsoft.VisualStudio.Workload.VCTools;installRecommended --add Microsoft.VisualStudio.Component.VC.Llvm.Clang --add Microsoft.VisualStudio.Component.VC.Llvm.ClangToolset"

Visit the Kaggle page for Gemma-2 and select Model Variations |> Gemma C++.

On this tab, the Variation dropdown includes the options below. Note bfloat16 weights are higher fidelity, while 8-bit switched floating point weights enable faster inference. In general, we recommend starting with the -sfp checkpoints.

[!NOTE] Important: We strongly recommend starting off with the gemma2-2b-it-sfp model to get up and running.

Gemma 2 models are named gemma2-2b-it for 2B and 9b-it or 27b-it. See the ModelPrefix function in configs.cc.

After filling out the consent form, the download should proceed to retrieve a tar archive file archive.tar.gz. Extract files from archive.tar.gz (this can take a few minutes):

tar -xf archive.tar.gz

This should produce a file containing model weights such as 2b-it-sfp.sbs and a tokenizer file (tokenizer.spm). You may want to move these files to a convenient directory location (e.g. the build/ directory in this repo).

The build system uses CMake. To build the gemma inference runtime, create a build directory and generate the build files using cmake from the top-level project directory. Note if you previous ran cmake and are re-running with a different setting, be sure to delete all files in the build/ directory with rm -rf build/*.

cmake -B build

After running cmake, you can enter the build/ directory and run make to build the ./gemma executable:

# Configure `build` directory
cmake --preset make

# Build project using make
cmake --build --preset make -j [number of parallel threads to use]

Replace [number of parallel threads to use] with a number - the number of cores available on your system is a reasonable heuristic. For example, make -j4 gemma will build using 4 threads. If the nproc command is available, you can use make -j$(nproc) gemma as a reasonable default for the number of threads.

If you aren't sure of the right value for the -j flag, you can simply run make gemma instead and it should still build the ./gemma executable.

If the build is successful, you should now have a gemma executable in the build/ directory.

# Configure `build` directory
cmake --preset windows

# Build project using Visual Studio Build Tools
cmake --build --preset windows -j [number of parallel threads to use]

If the build is successful, you should now have a gemma.exe executable in the build/ directory.

bazel build -c opt --cxxopt=-std=c++20 :gemma

If the build is successful, you should now have a gemma executable in the bazel-bin/ directory.

If you prefer Makefiles, @jart has made one available here:

https://github.com/jart/gemma3/blob/main/Makefile

You can now run gemma from inside the build/ directory.

gemma has the following required arguments:

Example invocation for the following configuration:

• weights file gemma2-2b-it-sfp.sbs (Gemma2 2B instruction-tuned model, 8-bit switched floating point).
• Tokenizer file tokenizer.spm (can omit for single-format weights files created after 2025-05-06, or output by migrate_weights.cc).

./gemma \
--tokenizer tokenizer.spm --weights gemma2-2b-it-sfp.sbs

This repository includes a version of Gemma based on Griffin (paper, code). Its architecture includes both recurrent layers and local attention, thus it is more efficient for longer sequences and has a smaller memory footprint than standard Gemma. We here provide a C++ implementation of this model based on the paper.

To use the recurrent version of Gemma included in this repository, build the gemma binary as noted above in Step 3. Download the compressed weights and tokenizer from the RecurrentGemma Kaggle as in Step 1, and run the binary as follows:

./gemma --tokenizer tokenizer.spm --model gr2b-it --weights 2b-it-sfp.sbs

This repository includes a version of the PaliGemma 2 VLM (paper). We provide a C++ implementation of the PaliGemma 2 model here.

To use the version of PaliGemma included in this repository, build the gemma binary as noted above in Step 3. Download the compressed weights and tokenizer from Kaggle and run the binary as follows:

./gemma \
--tokenizer paligemma_tokenizer.model \
--weights paligemma2-3b-mix-224-sfp.sbs \
--image_file paligemma/testdata/image.ppm

Note that the image reading code is very basic to avoid depending on an image processing library for now. We currently only support reading binary PPMs (P6). So use a tool like convert to first convert your images into that format, e.g.

convert image.jpeg -resize 224x224^ image.ppm

(As the image will be resized for processing anyway, we can already resize at this stage for slightly faster loading.)

The interaction with the image (using the mix-224 checkpoint) may then look something like this:

> Describe the image briefly
A large building with two towers in the middle of a city.
> What type of building is it?
church
> What color is the church?
gray
> caption image
A large building with two towers stands tall on the water's edge. The building
has a brown roof and a window on the side. A tree stands in front of the
building, and a flag waves proudly from its top. The water is calm and blue,
reflecting the sky above. A bridge crosses the water, and a red and white boat
rests on its surface. The building has a window on the side, and a flag on top.
A tall tree stands in front of the building, and a window on the building is
visible from the water. The water is green, and the sky is blue.

There is now a new format for the weights file, which is a single file that allows to contain the tokenizer (and the model type) directly. A tool to migrate from the multi-file format to the single-file format is available.

io/migrate_weights \
  --tokenizer .../tokenizer.spm --weights .../gemma2-2b-it-sfp.sbs \
  --output_weights .../gemma2-2b-it-sfp-single.sbs

After migration, you can omit the tokenizer argument like this:

./gemma --weights .../gemma2-2b-it-sfp-single.sbs

Problems building in Windows / Visual Studio

Currently if you're using Windows, we recommend building in WSL (Windows Subsystem for Linux). We are exploring options to enable other build configurations, see issues for active discussion.

Model does not respond to instructions and produces strange output

A common issue is that you are using a pre-trained model, which is not instruction-tuned and thus does not respond to instructions. Make sure you are using an instruction-tuned model (gemma2-2b-it-sfp) and not a pre-trained model (any model with a -pt suffix).

What sequence lengths are supported?

See max_seq_len in configs.cc and InferenceArgs.seq_len. For the Gemma 3 models larger than 1B, this is typically 32K but 128K would also work given enough RAM. Note that long sequences will be slow due to the quadratic cost of attention.

How do I convert my fine-tune to a .sbs compressed model file?

For PaliGemma 2 checkpoints, you can use python/convert_from_safetensors.py to convert from safetensors format (tested with building via bazel). For an adapter model, you will likely need to call merge_and_unload() to convert the adapter model to a single-file format before converting it.

Here is how to use it using a bazel build of the compression library assuming locally installed (venv) torch, numpy, safetensors, absl-py, etc.:

bazel build //compression/python:compression
BAZEL_OUTPUT_DIR="${PWD}/bazel-bin/compression"
python3 -c "import site; print(site.getsitepackages())"
# Use your sites-packages file here:
ln -s $BAZEL_OUTPUT_DIR [...]/site-packages/compression
python3 python/convert_from_safetensors.py --load_path [...].safetensors.index.json

What are some easy ways to make the model run faster?

1. Make sure you are using the 8-bit switched floating point -sfp models. These are half the size of bf16 and thus use less memory bandwidth and cache space.
2. Due to auto-tuning, the second and especially third query will be faster.
3. If you're on a laptop, make sure power mode is set to maximize performance and saving mode is off. For most laptops, the power saving modes get activated automatically if the computer is not plugged in.
4. Close other unused cpu-intensive applications.
5. On macs, anecdotally we observe a "warm-up" ramp-up in speed as performance cores get engaged.

We're also working on algorithmic and optimization approaches for faster inference, stay tuned.

gemma has different usage modes, controlled by the verbosity flag.

All usage modes are currently interactive, triggering text generation upon newline input.

By default, verbosity is set to 1, bringing up a terminal-based interactive interface when gemma is invoked:

$ ./gemma [...]
  __ _  ___ _ __ ___  _ __ ___   __ _   ___ _ __  _ __
 / _` |/ _ \ '_ ` _ \| '_ ` _ \ / _` | / __| '_ \| '_ \
| (_| |  __/ | | | | | | | | | | (_| || (__| |_) | |_) |
 \__, |\___|_| |_| |_|_| |_| |_|\__,_(_)___| .__/| .__/
  __/ |                                    | |   | |
 |___/                                     |_|   |_|

...

*Usage*
  Enter an instruction and press enter (%C reset conversation, %Q quits).

*Examples*
  - Write an email to grandma thanking her for the cookies.
  - What are some historical attractions to visit around Massachusetts?
  - Compute the nth fibonacci number in javascript.
  - Write a standup comedy bit about WebGPU programming.

> What are some outdoorsy places to visit around Boston?

[ Reading prompt ] .....................


**Boston Harbor and Islands:**

* **Boston Harbor Islands National and State Park:** Explore pristine beaches, wildlife, and maritime history.
* **Charles River Esplanade:** Enjoy scenic views of the harbor and city skyline.
* **Boston Harbor Cruise Company:** Take a relaxing harbor cruise and admire the city from a different perspective.
* **Seaport Village:** Visit a charming waterfront area with shops, restaurants, and a seaport museum.

**Forest and Nature:**

* **Forest Park:** Hike through a scenic forest with diverse wildlife.
* **Quabbin Reservoir:** Enjoy boating, fishing, and hiking in a scenic setting.
* **Mount Forest:** Explore a mountain with breathtaking views of the city and surrounding landscape.

...

For using the gemma executable as a command line tool, it may be useful to create an alias for gemma.cpp with arguments fully specified:

alias gemma2b="~/gemma.cpp/build/gemma -- --tokenizer ~/gemma.cpp/build/tokenizer.spm --weights ~/gemma.cpp/build/gemma2-2b-it-sfp.sbs --verbosity 0"

Replace the above paths with your own paths to the model and tokenizer paths from the download.

Here is an example of prompting gemma with a truncated input file (using a gemma2b alias like defined above):

cat configs.h | tail -n 35 | tr '\n' ' ' | xargs -0 echo "What does this C++ code do: " | gemma2b

The output of the above command should look like:

[ Reading prompt ] [...]
This C++ code snippet defines a set of **constants** used in a large language model (LLM) implementation, likely related to the **attention mechanism**.

Let's break down the code:
[...]

The easiest way to incorporate gemma.cpp in your own project is to pull in gemma.cpp and dependencies using FetchContent. You can add the following to your CMakeLists.txt:

include(FetchContent)

FetchContent_Declare(sentencepiece GIT_REPOSITORY https://github.com/google/sentencepiece GIT_TAG 53de76561cfc149d3c01037f0595669ad32a5e7c)
FetchContent_MakeAvailable(sentencepiece)

FetchContent_Declare(gemma GIT_REPOSITORY https://github.com/google/gemma.cpp GIT_TAG origin/main)
FetchContent_MakeAvailable(gemma)

FetchContent_Declare(highway GIT_REPOSITORY https://github.com/google/highway.git GIT_TAG 92d327e841d78e11ae888757a3e16d291951cf64)
FetchContent_MakeAvailable(highway)

Note for the gemma.cpp GIT_TAG, you may replace origin/main for a specific commit hash if you would like to pin the library version.

After your executable is defined (substitute your executable name for [Executable Name] below):

target_link_libraries([Executable Name] libgemma hwy hwy_contrib sentencepiece)
FetchContent_GetProperties(gemma)
FetchContent_GetProperties(sentencepiece)
target_include_directories([Executable Name] PRIVATE ${gemma_SOURCE_DIR})
target_include_directories([Executable Name] PRIVATE ${sentencepiece_SOURCE_DIR})

gemma.cpp can also be used as a library dependency in your own project. The shared library artifact can be built by modifying the make invocation to build the libgemma target instead of gemma.

First, run cmake:

cmake -B build

Then, run make with the libgemma target:

cd build
make -j [number of parallel threads to use] libgemma

If this is successful, you should now have a libgemma library file in the build/ directory. On Unix platforms, the filename is libgemma.a.

Some independent projects using gemma.cpp:

• gemma-cpp-python - Python bindings
• lua-cgemma - Lua bindings
• Godot engine demo project

If you would like to have your project included, feel free to get in touch or submit a PR with a README.md edit.

gemma.cpp was started in fall 2023 by Austin Huang and Jan Wassenberg, and subsequently released February 2024 thanks to contributions from Phil Culliton, Paul Chang, and Dan Zheng.

Griffin support was implemented in April 2024 thanks to contributions by Andrey Mikhaylov, Eugene Kliuchnikov, Jan Wassenberg, Jyrki Alakuijala, Lode Vandevenne, Luca Versari, Martin Bruse, Phil Culliton, Sami Boukortt, Thomas Fischbacher and Zoltan Szabadka.

Gemma-2 support was implemented in June/July 2024 with the help of several people.

PaliGemma support was implemented in September 2024 with contributions from Daniel Keysers.

Jan Wassenberg has continued to contribute many improvements, including major gains in efficiency, since the initial release.

This is not an officially supported Google product.