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llama-cpp

Execute LLM inference on CPUs, Apple Silicon, and non-NVIDIA GPUs using llama.cpp.

11.5k|842|Updated Nov 3, 2025
Search Tags:#llm#apple silicon#quantization#inference#cpu#edge deployment#gguf
One-click install
npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill llama-cpp-orchestra-research
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/12-inference-serving/llama-cpp
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill llama-cpp-orchestra-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes 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.
  • Quantization: Supports GGUF format for reduced memory usage and faster inference (4-10x speedup).
  • Use Case: Deploying a chatbot on a MacBook Pro, running inference on a Raspberry Pi, or utilizing an AMD GPU for LLM tasks without needing an NVIDIA card.

Quick Start

Use the llama-cpp skill to run inference on the model located at 'models/llama-2-7b-chat.Q4_K_M.gguf' and ask it to 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 a CPU without NVIDIA hardware?▼

To run LLM inference on a CPU without NVIDIA hardware, you can use the llama.cpp library to execute models directly on standard processors or edge devices. This approach enables efficient local execution by bypassing traditional CUDA dependencies.

Can I run large language models on Apple Silicon M1, M2, or M3 Macs?▼

Yes, you can run large language models on Apple Silicon M1, M2, or M3 Macs by leveraging the Metal framework. This Skill utilizes Metal for optimized inference, allowing MacBooks to execute LLMs efficiently without requiring a dedicated NVIDIA graphics card.

What is the best way to reduce memory usage when running local LLMs?▼

The best way to reduce memory usage and increase inference speed is by using GGUF quantization. This format compresses the model to significantly lower its memory footprint while providing a 4 to 10 times speedup during local execution.

Does llama.cpp support AMD GPUs for local LLM inference?▼

Yes, local LLM inference supports AMD GPUs by utilizing the ROCm platform. This allows you to execute large language models on AMD hardware in environments where CUDA is entirely unavailable.

How do I deploy a chatbot on edge devices like a Raspberry Pi?▼

You can deploy a chatbot on edge devices like a Raspberry Pi by running CPU-optimized LLM inference with GGUF quantized models. This configuration minimizes memory requirements and maximizes speed for constrained hardware environments.

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