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llama-cpp

Run CPU-based LLM inference on non-NVIDIA hardware using GGUF quantization.

Updated Mar 2, 2026
Search Tags:#gguf#apple-silicon#llama.cpp#edge-deployment#cpu-inference#gpu-offload#non-cuda
One-click install
npx skills add https://github.com/gigagiova/hermes-agent --skill llama-cpp-gigagiova
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/llama-cpp
Command: npx skills add https://github.com/gigagiova/hermes-agent --skill llama-cpp-gigagiova

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Pure C/C++ LLM inference with minimal dependencies, optimized for CPUs and non-NVIDIA hardware.

Core Features & Use Cases

  • Pure C/C++ inference engine optimized for CPU performance and minimal dependencies.
  • GGUF quantization support for memory efficiency on edge devices (Apple Silicon, AMD/Intel GPUs without CUDA).
  • Flexible deployment options including CLI and server modes with CPU-only and optional GPU offloading.

Quick Start

Install llama.cpp on your platform and run a simple CPU-based inference using the CLI.

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 Apple Silicon or AMD GPUs without CUDA?▼

You can run LLM inference on Apple Silicon or non-CUDA AMD GPUs by using a pure C/C++ runtime designed for CPU environments. It enables lightweight execution with optional GPU offloading for flexible edge deployment.

What format is required for CPU-based LLM inference on edge devices?▼

GGUF quantization formats are required for CPU-based LLM inference on edge devices. This format ensures memory efficiency, allowing large models to run on hardware with limited resources like Apple Silicon or CPU-only environments.

Can I deploy LLMs in CPU-only environments without installing heavy dependencies?▼

Yes, you can deploy LLMs in CPU-only environments without heavy dependencies. This approach uses a pure C/C++ inference engine optimized for minimal dependencies, supporting CLI and server modes for flexible deployment.

Does CPU-based LLM inference support GPU offloading for non-NVIDIA hardware?▼

CPU-based LLM inference does support optional GPU offloading for non-NVIDIA hardware. This allows you to accelerate execution on AMD or Intel GPUs while maintaining a baseline CPU-only execution mode.

What are the limitations of using a pure C/C++ inference engine for edge deployment?▼

The primary limitation is the strict requirement for GGUF quantized models. Because it is a pure C/C++ runtime optimized for minimal dependencies, you must convert models to this specific format before running CPU-based inference.

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