llama.cpp

Maple Preview — CPU Setup

Build llama.cpp

On Debian/Ubuntu ARM, install GCC/G++ if needed:

sudo apt update
sudo apt install -y build-essential

git clone git@github.com:deepgrove-ai/llama.cpp.git
cd llama.cpp

rm -rf ./build

uvx cmake -B build \
  -DCMAKE_BUILD_TYPE=Release \
  -DGGML_METAL=OFF

uvx cmake --build build -j

macOS uses Apple Clang. uvx provides CMake, so no separate CMake installation is needed.

Download the GGUF

uvx --from huggingface_hub hf download \
  deepgrove/maple-preview-GGUF \
  maple-preview-TQ2_0-head-Q4_K.gguf \
  --local-dir models

Hugging Face: deepgrove/maple-preview-GGUF

Variant	GGUF size
TQ1_0 + Q4_K head	4.64 GiB
TQ1_0 + FP16 head	5.06 GiB
TQ2_0 + Q4_K head	5.50 GiB
TQ2_0 + FP16 head	5.91 GiB

Start chatting

./build/bin/llama-completion \
  -m models/maple-preview-TQ2_0-head-Q4_K.gguf \
  --threads 16 \
  --temp 1.0 \
  --top-p 0.95 \
  --jinja \
  --conversation

--jinja applies Maple's embedded chat template exactly, including its thinking prefix.

Benchmark

M5 Pro, CPU-only, 16 threads, 512 prompt tokens, 128 generated tokens, 3 repetitions:

Matrix weights	LM head	GGUF size	Prefill (pp512)	Decode (tg128)
TQ1_0	FP16	5.06 GiB	515.41 ± 0.28 tokens/s	161.06 ± 0.57 tokens/s
TQ1_0	Q4_K	4.64 GiB	513.33 ± 3.62 tokens/s	231.13 ± 0.13 tokens/s
TQ2_0	FP16	5.91 GiB	618.57 ± 2.12 tokens/s	169.81 ± 2.94 tokens/s
TQ2_0	Q4_K	5.50 GiB	610.48 ± 3.76 tokens/s	252.74 ± 0.37 tokens/s

Adjust -t for #of threads.

FP16 LM head:

./build/bin/llama-bench \
  -m models/maple-preview-TQ2_0-head-F16.gguf \
  -p 512 \
  -n 128 \
  -r 3 \
  -t 16 \
  -dev none \
  -ngl 0

Q4_K LM head:

./build/bin/llama-bench \
  -m models/maple-preview-TQ2_0-head-Q4_K.gguf \
  -p 512 \
  -n 128 \
  -r 3 \
  -t 16 \
  -dev none \
  -ngl 0

LLM inference in C/C++

ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API

Quick start

A few options to get llama.cpp installed on your machine:

• Visit https://llama.app and follow the instructions
• Run with Docker - see our Docker documentation
• Download pre-built binaries from the releases page
• Build from source by cloning this repository - check out our build guide

Once installed:

# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF

# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

VLM session with llama cli

Built-in web UI against llama serve

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