llama.cpp

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 Q4_K_M)

GPU acceleration (Llama 2-7B Q4_K_M)

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