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llama-cpp

Run CPU-based LLM inference with GGUF quantization using C/C++.

1.0k|117|Updated Feb 26, 2026
Search Tags:#quantization#gguf#apple-silicon#llama.cpp#edge-deployment#non-nvidia#cpu-inference
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill llama-cpp-openlair
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llama-cpp
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/inference-serving/llama-cpp
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill llama-cpp-openlair

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides CPU-based LLM inference without requiring NVIDIA GPUs.

Core Features & Use Cases

  • Pure C/C++ inference with minimal dependencies, optimized for CPU and non-NVIDIA hardware.
  • GGUF quantization support for memory efficiency and speed on a variety of devices including Apple Silicon.
  • Flexible deployment across edge, desktop, and server environments without CUDA.

Quick Start

Install and run llama.cpp on your target hardware with a native build and quantized model.

Frequently Asked Questions about llama-cpp

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

FAQPage Schema
Does llama.cpp work on edge devices without CUDA dependencies?▼

GGUF quantization reduces memory consumption and increases inference speed by compressing model weights, allowing large language models to run efficiently on edge devices and non-NVIDIA hardware.

Can I use GGUF quantization to improve memory efficiency for CPU inference?▼

Yes, GGUF quantization is fully supported to optimize memory efficiency and speed, enabling flexible deployment of large language models across desktop, edge, and server environments without NVIDIA GPUs.

What is the best way to deploy LLMs on consumer hardware without NVIDIA GPUs?▼

Deploying LLMs on consumer hardware is best achieved through a pure C/C++ inference engine with minimal dependencies, utilizing GGUF quantization and optional hardware offload to bypass CUDA requirements.

What are the limitations of CPU-based LLM inference compared to GPU?▼

CPU-based LLM inference trades raw computational speed for broad compatibility, relying on GGUF quantization to maintain memory efficiency on edge devices and non-NVIDIA environments where CUDA is unavailable.

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