llama-cpp
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
Works with every agent the skills CLI supports.
npx skills add https://github.com/firecrawl/ai-research-skills --skill llama-cpp- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads llama-cpp automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
| Format | Bits | Size (7B) | Speed | Quality | Use Case |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.1 GB | Fast | Good | Recommended default |
| Q4_K_S | 4.3 | 3.9 GB | Faster | Lower | Speed critical |
| Q5_K_M | 5.5 | 4.8 GB | Medium | Better | Quality critical |
| Q6_K | 6.5 | 5.5 GB | Slower | Best | Maximum quality |
| Q8_0 | 8.0 | 7.0 GB | Slow | Excellent | Minimal degradation |
| Q2_K | 2.5 | 2.7 GB | Fastest | Poor | Testing only |
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 Q4_K_M)
| CPU | Threads | Speed | Cost |
|---|---|---|---|
| Apple M3 Max | 16 | 50 tok/s | $0 (local) |
| AMD Ryzen 9 7950X | 32 | 35 tok/s | $0.50/hour |
| Intel i9-13900K | 32 | 30 tok/s | $0.40/hour |
| AWS c7i.16xlarge | 64 | 40 tok/s | $2.88/hour |
GPU acceleration (Llama 2-7B Q4_K_M)
| GPU | Speed | vs CPU | Cost |
|---|---|---|---|
| NVIDIA RTX 4090 | 120 tok/s | 3-4× | $0 (local) |
| NVIDIA A10 | 80 tok/s | 2-3× | $1.00/hour |
| AMD MI250 | 70 tok/s | 2× | $2.00/hour |
| Apple M3 Max (Metal) | 50 tok/s | ~Same | $0 (local) |
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
FAQ
What does the llama-cpp skill do?
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.
How do I install llama-cpp?
Run npx skills add https://github.com/firecrawl/ai-research-skills --skill llama-cpp in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llama-cpp/ (personal) or .claude/skills/llama-cpp/ (project).
Which AI agents can use llama-cpp?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is llama-cpp safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains llama-cpp?
It is published by firecrawl in firecrawl/ai-research-skills, and it is an official skill from the maker of the technology it covers. It has 45 recorded installs.
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