llama.cpp

LLM inference in C/C++

manifesto / ggml / ops / maintainer PRs / compile times / lib llama API / llama-server REST API

Quick start

This fork adds inference support for the internally developed Spark2_5ForCausalLM model. The following commands build llama.cpp, convert a local Hugging Face checkpoint to GGUF, and run it on CPU or an NVIDIA GPU.

1. Build

For an NVIDIA CUDA build:

cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j 8

For a CPU-only build, use -DGGML_CUDA=OFF instead. The CUDA build also contains the CPU backend, so the same binaries can be used for both examples below.

2. Convert the Spark2_5 checkpoint to GGUF

Install the Python conversion dependencies:

python -m pip install -r requirements.txt

The Spark2_5 checkpoint stores its tokenizer under v8_2_token. The converter expects the tokenizer files next to config.json and the model .safetensors files, so copy them to the checkpoint root before conversion:

mkdir -p models
cp /path/to/spark2_5-hf/v8_2_token/{tokenizer.json,tokenizer_config.json,merges.txt} \
    /path/to/spark2_5-hf/

python convert_hf_to_gguf.py /path/to/spark2_5-hf \
    --outfile models/spark2_5-1.7b-bf16.gguf \
    --outtype bf16

3. Run inference

CPU:

./build/bin/llama-completion \
    -m models/spark2_5-1.7b-bf16.gguf \
    -ngl 0 -t 16 -c 1024 \
    -cnv -st --jinja --simple-io --no-display-prompt \
    -p '请用三句话解释什么是计算图。' \
    -n 96 --temp 0 --seed 1

NVIDIA GPU (GPU 0):

CUDA_VISIBLE_DEVICES=0 ./build/bin/llama-completion \
    -m models/spark2_5-1.7b-bf16.gguf \
    -ngl 99 -t 16 -c 1024 \
    -cnv -st --jinja --simple-io --no-display-prompt \
    -p '请用三句话解释什么是计算图。' \
    -n 96 --temp 0 --seed 1

-ngl 99 offloads all Spark2_5 layers to the selected GPU. Use an integer for -ngl; values such as all are not accepted by llama-bench.

4. Verify the architecture and GPU backend

./build/bin/test-llama-archs -a spark2_5

CUDA_VISIBLE_DEVICES=0 ./build/bin/llama-bench \
    -m models/spark2_5-1.7b-bf16.gguf \
    -ngl 99 -p 32 -n 8

The architecture test should report OK for the CPU and CUDA backends. A Roundtrip: SKIP result is expected because model-saver roundtrip support is currently disabled for Spark2_5.

Description

The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

• Plain C/C++ implementation without any dependencies
• Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
• AVX, AVX2, AVX512 and AMX support for x86 architectures
• RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
• 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
• Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
• Vulkan and SYCL backend support
• CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is build on top of the ggml library.

Supported backends

Backend	Target devices
BLAS	All
BLIS	All
CANN	Ascend NPU
CUDA	Nvidia GPU
HIP	AMD GPU
Hexagon [In Progress]	Snapdragon
IBM zDNN	IBM Z & LinuxONE
MUSA	Moore Threads GPU
Metal	Apple Silicon
OpenCL	Adreno GPU
OpenVINO [In Progress]	Intel CPUs, GPUs, and NPUs
RPC	All
SYCL	Intel GPU
VirtGPU	VirtGPU APIR
Vulkan	GPU
WebGPU	All
ZenDNN	AMD CPU

Documentation

Tools

• cli
• completion
• server
• GBNF grammars

Development

• How to build
• Running on Docker
• Build on Android
• Multi-GPU usage
• Performance troubleshooting
• GGML tips & tricks
• XCFramework
• Completions
• Models
• Release process

Contributing

• Contributors can open PRs
• Collaborators will be invited based on contributions
• Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
• Any help with managing issues, PRs and projects is very appreciated!
• Read the CONTRIBUTING.md for more information

Acknowledgements

• yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
• nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
• nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
• mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
• sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain