# Llama Cpp

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llama-cpp -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llama-cpp -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llama-cpp -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llama-cpp -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# llama.cpp

Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.

## When to use llama.cpp

**Use llama.cpp when:**
- Running on CPU-only machines
- Deploying on Apple Silicon (M1/M2/M3/M4)
- Using AMD or Intel GPUs (no CUDA)
- Edge deployment (Raspberry Pi, embedded systems)
- Need simple deployment without Docker/Python

**Use TensorRT-LLM instead when:**
- Have NVIDIA GPUs (A100/H100)
- Need maximum throughput (100K+ tok/s)
- Running in datacenter with CUDA

**Use vLLM instead when:**
- Have NVIDIA GPUs
- Need Python-first API
- Want PagedAttention

## Quick start

### Installation

```bash
# macOS/Linux
brew install llama.cpp

# Or build from source
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# With Metal (Apple Silicon)
make LLAMA_METAL=1

# With CUDA (NVIDIA)
make LLAMA_CUDA=1

# With ROCm (AMD)
make LLAMA_HIP=1
```

### Download model

```bash
# Download from HuggingFace (GGUF format)
huggingface-cli download \
    TheBloke/Llama-2-7B-Chat-GGUF \
    llama-2-7b-chat.Q4_K_M.gguf \
    --local-dir models/

# Or convert from HuggingFace
python convert_hf_to_gguf.py models/llama-2-7b-chat/
```

### Run inference

```bash
# Simple chat
./llama-cli \
    -m models/llama-2-7b-chat.Q4_K_M.gguf \
    -p "Explain quantum computing" \
    -n 256  # Max tokens

# Interactive chat
./llama-cli \
    -m models/llama-2-7b-chat.Q4_K_M.gguf \
    --interactive
```

### Server mode

```bash
# Start OpenAI-compatible server
./llama-server \
    -m models/llama-2-7b-chat.Q4_K_M.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 32  # Offload 32 layers to GPU

# Client request
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-2-7b-chat",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.7,
    "max_tokens": 100
  }'
```

## Quantization formats

### GGUF format overview

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/12-inference-serving/llama-cpp/SKILL.md