Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/orchestra-research/ai-research-skills --skill llama-cppAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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=1Download 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 \
--interactiveServer 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
| Format | Bits | Size (7B) | Speed | Quality | Use Case |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 4.1 GB | Fast | Good | Recommended default |
| Q4KS | 4.3 | 3.9 GB | Faster | Lower | Speed critical |
| Q5KM | 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
bash
# 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 memoryHardware acceleration
Apple Silicon (Metal)
bash
# 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)
bash
# 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: restAMD GPUs (ROCm)
bash
# Build with ROCm
make LLAMA_HIP=1
# Run with AMD GPU
./llama-cli -m model.gguf -ngl 999Common patterns
Batch processing
bash
# Process multiple prompts from file
cat prompts.txt | ./llama-cli \
-m model.gguf \
--batch-size 512 \
-n 100Constrained generation
bash
# JSON output with grammar
./llama-cli \
-m model.gguf \
-p "Generate a person: " \
--grammar-file grammars/json.gbnf
# Outputs valid JSON onlyContext size
bash
# 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 contextPerformance benchmarks
CPU performance (Llama 2-7B Q4KM)
| 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 Q4KM)
| 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](references/quantization.md) - GGUF formats, conversion, quality comparison
- [Server Deployment](references/server.md) - API endpoints, Docker, monitoring
- [Optimization](references/optimization.md) - 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
