Lesson 1708 of 2116
AI tools: running local models and when it pays off
Local models pay off for privacy-bound data, batch jobs at scale, and offline scenarios. They lose on ergonomics and frontier quality.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2local LLMs
- 3privacy
- 4cost vs quality tradeoff
Concept cluster
Terms to connect while reading
Section 1
The premise
Local LLMs (via Ollama, llama.cpp, vLLM) win when data must not leave your premises or when batch volumes make per-token API pricing uneconomic. They lose on the latest frontier capabilities and on developer ergonomics.
What AI does well here
- Run on commodity GPUs at smaller parameter counts
- Serve high-throughput batch workloads cheaply
- Operate fully offline once weights are downloaded
What AI cannot do
- Match frontier-model reasoning at small parameter counts
- Update knowledge without you re-downloading weights
- Provide hosted-grade reliability without your ops effort
Key terms in this lesson
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