Understanding Local AI Models

"Running AI locally" sounds like a single idea but covers a wide range of actual setups, from a small model running on a laptop's CPU to a serious multi-GPU server running something close to frontier-scale capability. Understanding what's actually involved — and what it trades off against a hosted API — is more useful than treating "local" as a single yes/no decision.

Fact: running a model locally means downloading its weights (the trained parameters, typically an open-weight model such as those from Meta's Llama family, Mistral, or Qwen) and running inference on your own hardware, rather than sending a request to a provider's API. The practical requirement scales with model size: small models run acceptably on a modern laptop; capable mid-size models need a dedicated GPU with meaningful memory; anything approaching frontier-model capability needs hardware most individuals and small businesses don't own.

What you actually gain

The core trade is privacy and control for capability and convenience. Data never leaves your machine or network, which matters a great deal for anything sensitive — client information, unpublished work, anything under a confidentiality obligation. There's no per-token cost once the hardware is paid for, no dependency on a provider's uptime, and no risk of a model being deprecated or a pricing policy changing under you. You also get to run the exact same model indefinitely, which matters if reproducibility over time is important to what you're building.

What you actually give up

Local models, even capable open-weight ones, generally lag the best hosted frontier models on the hardest reasoning and coding tasks — that gap has narrowed substantially but hasn't closed. You're also taking on the ops work a hosted API abstracts away: managing updates, handling hardware failures, optimising for the specific hardware you have. For anything that needs to scale to serve many users at once, the hardware and engineering investment to do that well locally is significant.

Analysis: the decision genuinely turns on what's being protected and how often the workload runs. A single person doing occasional, non-sensitive tasks is almost always better served by a hosted API — the convenience and capability gap isn't worth managing hardware for. A business processing sensitive documents at meaningful volume, or a team that needs a model to behave identically for years regardless of a provider's roadmap, has a real case for running locally, cost and privacy both pointing the same direction.

Opinion: the framing of "local vs API" as a permanent either-or choice undersells how often the right answer is both, used for different jobs — a hosted frontier model for the hardest, highest-stakes reasoning, a smaller local model for high-volume, well-defined, privacy-sensitive tasks like document classification or first-pass extraction. Treating it as one decision for an entire organisation usually means overpaying for capability that isn't needed somewhere, or underserving a task that genuinely needed more capability than the local model could provide.

Prediction, held loosely: consumer and small-business hardware keeps getting more capable of running genuinely useful local models, which will keep expanding the set of tasks where "local" is a realistic default rather than a compromise — document processing, coding assistance for well-scoped tasks and customer support are the likeliest early movers, well before local models are a serious substitute for frontier-level reasoning on the hardest problems.