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llama-cpp

Execute LLM inference on CPU, Apple Silicon, and consumer GPUs with GGUF quantization.

4|Updated Apr 19, 2026
Search Tags:#LLM inference#Apple Silicon#CPU inference#Edge deployment#GGUF quantization
One-click install
npx skills add https://github.com/ragnarokhaa/hermes --skill llama-cpp-ragnarokhaa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/ragnarokhaa/hermes/tree/main/hermes-cerul-tech-news-package/hermes-cerul-tech-news-package/hermes-agent/skills/mlops/inference/llama-cpp
Command: npx skills add https://github.com/ragnarokhaa/hermes --skill llama-cpp-ragnarokhaa

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires llama-cpp-python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of running large language models (LLMs) on non-NVIDIA hardware, allowing edge deployment and efficient CPU inference without CUDA.

Core Features & Use Cases

  • Multi-Hardware Support: Runs on CPU, Apple Silicon, and consumer GPUs.
  • Edge Deployment: Optimized for Raspberry Pi, embedded systems, and M1/M2/M3 Macs.
  • GGUF Quantization: Supports GGUF quantization for reduced memory and increased speed.
  • Use Case: For organizations that require deployment on edge devices or have limited access to NVIDIA GPUs, llama-cpp provides a cost-effective and resource-efficient solution.

Quick Start

Install llama.cpp and download a pre-trained model. Use the llama-cli tool to run inference:

llama-cli -m models/llama-2-7b-chat.Q4_K_M.gguf -p "Explain quantum computing"

Frequently Asked Questions about llama-cpp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run LLM inference on CPU or Apple Silicon without CUDA?▼

To run LLM inference on CPU or Apple Silicon without CUDA, install llama.cpp and download a pre-trained GGUF model. You can then use the llama-cli tool to execute inference directly on your hardware without requiring an NVIDIA GPU.

Can I deploy large language models on edge devices like Raspberry Pi?▼

Yes, you can deploy large language models on edge devices like Raspberry Pi. This Skill optimizes edge deployment for embedded systems and M1/M2/M3 Macs, providing a cost-effective and resource-efficient solution for environments with limited GPU access.

What is GGUF quantization and when do I need it for CPU inference?▼

GGUF quantization is a model compression technique needed for efficient CPU inference on non-NVIDIA hardware. It reduces memory requirements and increases inference speed, making it essential for running large language models on edge devices.

Does llama-cpp-python support running inference on consumer GPUs?▼

Yes, llama-cpp-python supports running LLM inference on select consumer GPUs, alongside CPU and Apple Silicon. This multi-hardware support enables edge deployment scenarios without relying on CUDA or NVIDIA-specific hardware.

What are the limitations of running LLM inference without CUDA?▼

Running LLM inference without CUDA limits you to CPU, Apple Silicon, or select consumer GPUs. While GGUF quantization improves performance, edge deployment on devices like Raspberry Pi may still face processing speed constraints compared to high-end NVIDIA GPUs.

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