llama-cpp skill

by Orchestra-Research·Orchestra-Research/AI-Research-SKILLs·13k stars·MIT

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.

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Install the llama-cpp skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git /tmp/AI-Research-SKILLs
mkdir -p ~/.claude/skills
cp -r /tmp/AI-Research-SKILLs/12-inference-serving/llama-cpp ~/.claude/skills/llama-cpp

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

# 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

# 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

# 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

# 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

Choosing quantization

# General use (balanced)
Q4_K_M  # 4-bit, medium quality

# Maximum speed (more degradation)
Q2_K or Q3_K_M

# Maximum quality (slower)
Q6_K or Q8_0

# Very large models (70B, 405B)
Q3_K_M or Q4_K_S  # Lower bits to fit in memory

Hardware acceleration

Apple Silicon (Metal)

# Build with Metal
make LLAMA_METAL=1

# Run with GPU acceleration (automatic)
./llama-cli -m model.gguf -ngl 999  # Offload all layers

# Performance: M3 Max 40-60 tokens/sec (Llama 2-7B Q4_K_M)

NVIDIA GPUs (CUDA)

# Build with CUDA
make LLAMA_CUDA=1

# Offload layers to GPU
./llama-cli -m model.gguf -ngl 35  # Offload 35/40 layers

# Hybrid CPU+GPU for large models
./llama-cli -m llama-70b.Q4_K_M.gguf -ngl 20  # GPU: 20 layers, CPU: rest

AMD GPUs (ROCm)

# Build with ROCm
make LLAMA_HIP=1

# Run with AMD GPU
./llama-cli -m model.gguf -ngl 999

Common patterns

Batch processing

# Process multiple prompts from file
cat prompts.txt | ./llama-cli \
    -m model.gguf \
    --batch-size 512 \
    -n 100

Constrained generation

# JSON output with grammar
./llama-cli \
    -m model.gguf \
    -p "Generate a person: " \
    --grammar-file grammars/json.gbnf

# Outputs valid JSON only

Context size

# Increase context (default 512)
./llama-cli \
    -m model.gguf \
    -c 4096  # 4K context window

# Very long context (if model supports)
./llama-cli -m model.gguf -c 32768  # 32K context

Performance benchmarks

CPU performance (Llama 2-7B Q4KM)

GPU acceleration (Llama 2-7B Q4KM)

Supported models

LLaMA family:

Llama 2 (7B, 13B, 70B)
Llama 3 (8B, 70B, 405B)
Code Llama

Mistral family:

Mistral 7B
Mixtral 8x7B, 8x22B

Other:

Falcon, BLOOM, GPT-J
Phi-3, Gemma, Qwen
LLaVA (vision), Whisper (audio)

Find models: https://huggingface.co/models?library=gguf

References

Quantization Guide - GGUF formats, conversion, quality comparison
Server Deployment - API endpoints, Docker, monitoring
Optimization - Performance tuning, hybrid CPU+GPU

Resources

GitHub: https://github.com/ggerganov/llama.cpp
Models: https://huggingface.co/models?library=gguf
Discord: https://discord.gg/llama-cpp

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