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llama-cpp

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

Updated Apr 30, 2026
Search Tags:#LLM inference#Apple Silicon#CPU inference#AMD GPUs#Intel GPUs#Edge deployment#GGUF quantization
One-click install
npx skills add https://github.com/Ced3-han/Harness-Settings --skill llama-cpp-ced3-han
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/Ced3-han/Harness-Settings/tree/main/skills/llama-cpp
Command: npx skills add https://github.com/Ced3-han/Harness-Settings --skill llama-cpp-ced3-han

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides fast LLM inference on CPUs, Apple Silicon, and consumer GPUs without NVIDIA hardware, ideal for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or CUDA-unavailable environments.

Core Features & Use Cases

  • Multi-Hardware Support: Inference on CPU, Apple Silicon, and consumer GPUs.
  • GGUF Quantization: Supports GGUF quantization for reduced memory and 4-10× speedup.
  • Edge Deployment: Optimized for Raspberry Pi, embedded systems, and CPU-only machines.
  • Use Case: Deploy a lightweight chatbot on a Raspberry Pi or a simple research assistant on an Intel CPU without CUDA.

Quick Start

Run inference with llama.cpp on a Raspberry Pi using the following command:

./llama-cli -m models/llama-2-7b-chat.Q4_K_M.gguf -p "What is 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 without NVIDIA hardware?▼

You can run LLM inference on CPU-only systems by utilizing GGUF quantization for performance optimization. This approach supports fast inference on non-NVIDIA hardware, making it ideal for edge deployment and machines without CUDA.

Can I deploy a lightweight chatbot on a Raspberry Pi using GGUF quantization?▼

Yes, you can deploy a lightweight chatbot on a Raspberry Pi using GGUF quantization. This optimization reduces memory requirements and provides a 4-10x speedup, making edge deployment on embedded systems highly efficient.

Does llama-cpp-python support Apple Silicon and AMD GPUs for inference?▼

The llama-cpp-python dependency supports multi-hardware inference, including Apple Silicon M1/M2/M3 Macs, AMD GPUs, and Intel GPUs. It enables fast LLM inference in CUDA-unavailable environments.

What is the best way to accelerate LLM inference on consumer GPUs?▼

The best way to accelerate LLM inference on consumer GPUs is by applying GGUF quantization. This technique reduces memory usage and achieves a 4-10x speedup on supported hardware like AMD and Intel GPUs.

When do I need GGUF quantization for edge deployment?▼

You need GGUF quantization for edge deployment when running LLMs on resource-constrained devices like Raspberry Pi or CPU-only machines. It significantly reduces memory requirements and accelerates inference without relying on NVIDIA hardware.

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