Things I Think I Think... Preferring Local OSS LLMs

9 hours ago

The author prefers local LLMs over cloud-based ones due to increased reliability and reduced dependence on external networks.
Local hosting avoids single points of failure, as demonstrated by the March 2026 Anthropic server outage that disrupted Claude Code.
Distributed systems are inherently fragile, relying on multiple components (e.g., ISPs, cloud hosts) that can fail, whereas local setups minimize these risks.
The AI industry is considered a bubble, with unsustainable costs that may lead to price hikes, data monetization, or collapse, making local stacks a safer option.
Running local LLMs requires open-source tools (like Ollama) and powerful hardware (e.g., an RTX 4090 GPU), as commercial local options are scarce.
Self-hosting educates users on AI infrastructure, enabling deeper understanding and skill development for future architect roles in software development.
Data privacy is a concern, as cloud AI services might sell user data (e.g., confidential conversations) to advertisers, which local hosting can prevent.
Local setups are cost-effective long-term, avoiding potential price increases from cloud providers struggling with profitability.

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