llama-cpp

name	llama-cpp
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.
category	ml-inference
version	1.0.0
author	Synthetic Sciences
license	MIT
tags	["Inference Serving","Llama.cpp","CPU Inference","Apple Silicon","Edge Deployment","GGUF","Quantization","Non-NVIDIA","AMD GPUs","Intel GPUs","Embedded"]
dependencies	["llama-cpp-python"]

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  \
  -d