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llama-cpp

Run LLM inference on CPUs, Apple Silicon, and AMD GPUs with GGUF quantization.

228k|44.7k|Updated Jul 22, 2025
Search Tags:#llm#apple silicon#inference#edge deployment#gguf#llama.cpp#cpu inference
One-click install
npx skills add https://github.com/NousResearch/hermes-agent --skill llama-cpp-nousresearch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/NousResearch/hermes-agent/tree/main/skills/mlops/llama-cpp
Command: npx skills add https://github.com/NousResearch/hermes-agent --skill llama-cpp-nousresearch

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill enables running large language models (LLMs) on consumer hardware, including CPUs, Apple Silicon, and non-NVIDIA GPUs, overcoming the limitations of traditional CUDA-dependent solutions.

Core Features & Use Cases

  • CPU & Edge Inference: Optimized for running LLMs on standard CPUs and edge devices.
  • Non-NVIDIA GPU Support: Leverages Metal for Apple Silicon and ROCm for AMD GPUs.
  • GGUF Quantization: Supports various quantization levels for reduced memory footprint and faster inference.
  • Use Case: Deploying a chatbot on a MacBook Pro or a Raspberry Pi, or running LLM inference on a server without expensive NVIDIA hardware.

Quick Start

Use the llama-cpp skill to run inference with the Llama-2-7B-Chat-GGUF model.

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?▼

You can run LLM inference on CPU or Apple Silicon without CUDA by using this skill with the llama.cpp library. It leverages Metal for Apple hardware and standard CPU processing, enabling efficient chatbot deployment on consumer machines like MacBooks.

What is GGUF quantization and why use it for edge deployment?▼

GGUF quantization is a model format that reduces memory footprint and accelerates LLM inference. It is used for edge deployment on devices like Raspberry Pi to overcome memory constraints and achieve significant speedups compared to standard PyTorch.

Can I deploy a chatbot on edge devices using llama-cpp-python?▼

Yes, you can deploy a chatbot on edge devices using this skill with the llama-cpp-python dependency. It supports edge deployments by running LLM inference directly on standard CPUs and consumer hardware without requiring expensive NVIDIA components.

Does llama.cpp support AMD GPUs for running large language models?▼

Yes, llama.cpp supports AMD GPUs for running large language models by leveraging ROCm. This allows you to perform LLM inference on non-NVIDIA hardware, overcoming the limitations of traditional CUDA-dependent solutions.

Best way to reduce memory footprint when running LLMs on consumer hardware?▼

The best way to reduce memory footprint when running LLMs on consumer hardware is applying GGUF quantization via this skill. It offers various quantization levels, significantly lowering memory requirements and increasing inference speed on standard CPUs.

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