The Privacy Case for Local LLMs

While many users rely on cloud-based AI platforms such as ChatGPT, Gemini, Claude, and Perplexity for daily tasks, Wired reports that these large language models (LLMs) can be run locally on a user's own computer.

According to Wired, the primary advantages of local installation are increased data privacy and offline access, as users are not transmitting data to the cloud for review or analysis by third parties. Additionally, running models locally removes the need for monthly subscriptions or adhering to usage rates. Wired notes that free models are available from major companies, including Google and Meta.

Implementing a local system requires specific hardware and software. Wired suggests that while Windows, Linux, and macOS are compatible, macOS is often preferred by enthusiasts due to Apple Silicon chips. Hardware requirements vary; while 8 GB of RAM is a minimum, 16 GB is considered better, and 32 GB or more is necessary for the fastest and largest models. For Windows users, Wired highlights that a dedicated Nvidia GPU provides optimized memory (VRAM) for AI processes.

To operate these models, users need an interface. Wired identifies LM Studio Bionic as a free, user-friendly option for Windows and macOS, while citing vLLM, Llama.cpp, Ollama, and GPT4All as other trusted, though more technical, alternatives. For the models themselves, Wired points to Hugging Face as the most well-known repository, offering over 3 million downloadable models.