The question is no longer whether open models are good enough to run locally; for many tasks they are. The real question is which workloads belong on your own hardware and which are better sent to a hosted API. This framework helps you decide without ideology.
Five questions to ask
1. How sensitive is the data? Contracts, medical notes, source code under NDA, customer records and internal strategy documents are strong candidates for local processing. If data cannot leave your premises for legal or contractual reasons, the decision is already made.
2. How hard is the task? Frontier hosted models still lead on the hardest reasoning, very long context and some specialized domains. Everyday tasks such as rewriting, translation, summarization, extraction and classification are well within reach of mid-sized local models.
3. How much volume? Hosted APIs charge per token. Light, occasional use is cheap in the cloud. High, steady volume, such as processing thousands of documents a day, can make local hardware pay for itself over time. Do the arithmetic with your real volumes, including electricity.
4. Do you need offline or low-latency operation? Field work, factories, labs and travel favor local models. Local inference also removes network round trips for interactive tools.
5. Who will maintain it? Local deployment means someone updates drivers, models and software. If nobody owns that job, cloud may be safer.
A hybrid is often best
Many teams settle on a mix: sensitive or high-volume work runs locally; rare, very hard questions go to a hosted model, with a policy that sensitive material is never included. Tools with OpenAI-compatible APIs make switching between local and hosted back ends straightforward.
Getting started locally
Begin with the hardware you already have. Install a runtime such as Ollama or LM Studio, try a 7B or 8B model, and test it against a set of real tasks. If results are close to what you need, scale up model size or hardware. If not, you have learned cheaply where the boundary is.
Common mistakes
- Buying expensive hardware before testing real tasks.
- Comparing a small local model with the largest hosted model and concluding local AI is useless.
- Ignoring access control when connecting local models to internal documents.
Summary
Choose local when privacy, volume, offline use or control matter most; choose cloud when task difficulty dominates and data is not sensitive; mix both when your needs vary.
References
- MicroAI One, guides on local LLM hardware, software and deployment: https://microaione.com/
- MicroAI One, custom builds and enterprise delivery: https://microaione.com/solutions/
Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.