HUMANIKRONNot every task is worth cloud API tokens. I keep LM Studio on my PC with several models and turn them on where it pays off.
What runs locally for me
- gemma — the main "smart" model: vision, tools, reasoning.
- qwen — a faster alternative.
- small models for coding and embeddings (RAG).
All through an OpenAI-compatible API on localhost:1234 — it connects to anything in a minute.
When local models win
- Drafts and rough processing — descriptions, rewriting, classification. "Good enough" quality, zero cost.
- Private data — nothing leaves the machine.
- High volume — bulk operations where the cloud would be expensive.
When I still take the Claude API
- Final texts where quality and nuance matter.
- Complex reasoning, agents, where a mistake is costly.
It is not either/or. My typical pipeline is draft locally, finish in the cloud. Even the AI agent that prepares drafts for this blog runs on a local model, and I just proofread before publishing.
Conclusion
Local LLMs are not a replacement for the cloud but a tool for balancing cost/quality/privacy. If you want to automate your processes like this — let's talk.