Why Run Local LLMs: Privacy, Cost, Latency, and Control

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.

Privacy is the headline reason

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.

Cost flips at scale

• For low volume, cloud is dramatically cheaper — no hardware to buy
• For very high volume, local can be cheaper because the marginal cost is electricity, not tokens
• The crossover depends on your workload and hardware — there is no universal answer

Latency cuts both ways

• Cloud: fast network, top-tier accelerators, but a network roundtrip (~100ms) on every call
• Local: no network, but inference speed is bounded by your GPU/CPU
• On a recent M-series Mac, a small local model can beat a slow cloud call to time-to-first-token

Apply this

1. 1Identify one workflow where privacy is the constraint, not capability
2. 2Identify one workflow where you would never give up cloud-frontier capability
3. 3Write down what hardware you already own — it determines what local class you can run today

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.