Running Large Language Models Privately

Running large language models (LLMs) locally can enhance privacy and reduce dependency on external providers. Smaller models can be run on standard laptops while more powerful models need advanced hardware. The post discusses various frameworks like llama.cpp and Ollama for local deployment, focusing on speed, power consumption, and performance across different quantization levels. The conclusion highlights the cost-effectiveness and customization options of running LLMs privately compared to cloud-based solutions.