GGUF Quantization Tutorial: Run Fine-Tuned LLMs on CPU with llama.cpp

About this lesson

In this video, we walk through how to quantize and serve a fine-tuned large language model using GGUF and llama.cpp, enabling efficient CPU and edge-device inference without requiring a GPU. You’ll learn how to: Understand what GGUF is and how it relates to llama.cpp Choose the right quantization method (Q5_K_M and other variants) Convert a Hugging Face model to GGUF format Quantize a fine-tuned base model for efficient inference Handle LoRA adapters when converting and serving models Serve your model locally using an OpenAI-compatible API Test the running model using curl and Python Timestamps: 0:00 - What GGUF and llama.cpp are and when to use them 1:06 - GGUF vs model architecture and supported frameworks 2:15 - Quantization naming conventions and quality trade-offs 3:42 - Choosing the right quantization method (Q5_K_M) 4:16 - Building llama.cpp and environment setup 5:31 - Downloading base models and LoRA adapters 6:11 - Converting Hugging Face models to GGUF 6:47 - Quantizing and serving the model with llama.cpp Watch this video if you want to deploy fine-tuned LLMs on CPUs, run models on edge devices, or reduce inference cost without sacrificing too much quality. This video is part of the LLM Engineering and Deployment Certification Program by Ready Tensor. Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI and ML professionals build, deploy, and evaluate intelligent systems through certifications, competitions, and real-world project publications. Learn more: https://www.readytensor.ai/ Like the video? Subscribe for more hands-on tutorials on LLM deployment, optimization, and serving.

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