Local AI Inference: Surprising Performance Without a GPU

A Reddit user has shared their experience running various AI tools locally on an old CPU-only desktop PC, demonstrating that expensive hardware isn't always necessary to experiment with AI.

The hardware setup used includes an Intel i5-8500 processor, 32 GB of RAM, and the Linux Mint operating system. With this configuration, the user is able to run 12B parameter LLM (Large Language Model) models using KoboldCPP, achieving acceptable response times for chatbots and other text-based applications.

In addition to LLMs, the user is also able to generate images with Stable Diffusion 1.5, although with longer processing times (approximately 3 minutes for a 512x512 image). Other tools successfully used include Chatterbox TTS for voice cloning and Upscayl for image upscaling.

This demonstrates that, with the right optimization and choice of suitable models, it is possible to run AI inference locally even on less powerful hardware.

Implications for Data Sovereignty

Running AI models locally offers greater control over data, a crucial aspect for those concerned about privacy and data sovereignty. Unlike cloud services, local processing ensures that data remains within your own infrastructure, reducing the risk of exposure to third parties and simplifying compliance with regulations such as GDPR.