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Cloud LLMs are convenient. Local LLMs are different — not always better, but better in specific dimensions that matter for specific workloads. Here is the honest case for and against running models on your own hardware.
A local LLM is a model whose weights live on your machine and whose inference runs on your CPU or GPU. No API call leaves the box. Compare that to a cloud LLM, where every prompt goes to a vendor's servers, gets processed, and comes back. Both produce the same kind of output; the difference is everything around the model — who sees the data, who pays for the GPUs, who decides when it goes down for maintenance.
| Dimension | Cloud LLM | Local LLM |
|---|---|---|
| Peak capability | Frontier-class | Behind, but improving fast |
| Privacy | Vendor terms apply | Data never leaves your machine |
| Cost shape | Per-token, scales with use | Hardware up front, then near-zero |
| Latency floor | Network roundtrip | Limited by your hardware |
| Availability | Depends on vendor | Depends on you |
| Auditability | Black-box change log | Reproducible — the weights do not change |
If you handle medical records, legal discovery, internal HR data, or anything else where 'send it to a third party' is awkward, local inference removes the third party. Even if the cloud vendor's privacy promises are airtight in practice, in theory many regulated workflows are easier when there is no theory.
The big idea: local LLMs trade peak capability for privacy, control, and a different cost shape. Pick the trade for the workload, not for the ideology.
15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-local-why-run-local-llms-creators
What is the core idea behind "Why Run Local LLMs: Privacy, Cost, Latency, and Control"?
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What is the key insight about "Capability gap is real" in the context of Why Run Local LLMs: Privacy, Cost, Latency, and Control?
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