TL;DR: The classic CIA triad: Confidentiality, Integrity, Availability; cost/price concerns; the leading open-weight models aren't nearly as bad as you might think.

You don't need LM Studio to run local models, it just (was, formerly), a nice UI to download and manage HF models and llama.cpp updates, quickly and easily manually switch between CPU / Vulkan / ROCm / CUDA (depending on your platform).

Regarding your actual question, there are several reasons.

First off, your allusion to privacy - absolutely, yes, some people use it for adult role-play, however, consider the more productive motivations for privacy, too: a lot of businesses with trade secrets they may want to discuss or work on with local models without ever releasing that information to cloud providers, no matter how much those cloud providers pinky promise to never peek at it. Google, Microsoft, Meta, et al have consistently demonstrated that they do not value or respect customer privacy expectations, that they will eagerly comply with illegal, unconstitutional NSA conspiracies to facilitate bulk collection of customer information / data. There is no reason to believe Anthropic, OpenAI, Google, xAI would act any differently today. In fact, there is already a standing court order forcing OpenAI to preserve all customer communications, in a format that can be delivered to the court (i.e. plaintext, or encryption at rest + willing to provide decryption keys to the court), in perpetuity (https://techstartups.com/2025/06/06/court-orders-openai-to-p...)

There are also businesses which have strict, absolute needs for 24/7 availability and low latency, which remote APIs never have offered. Even if the remote APIs were flawless, and even if the businesses have a robust multi-WAN setup with redundant UPS systems, network downtime or even routing issues are more or less an inevitable fact of life, sooner or later. Having local models means you have inference capability as long as you have electricity.

Consider, too, the integrity front: frontier labs may silently modify API-served models to be lower quality for heavy users with little means of detection by end users (multiple labs have been suspected / accused of this; a lack of proof isn't evidence that it didn't happen) or that the API-served models can be modified over time to patch behaviors that may have been previously relied upon for legitimate workloads (imagine a red team that used a jailbreak to get a model to produce code for process hollowing, for instance). This second example absolutely has happened with almost every inference provider.

The open weight local models also have zero marginal cost besides electricity once the hardware is present, unlike PAYG API models, which create financial lock-in and dependency that is in direct contrast with the financial interests of the customers. You can argue about the amortized costs of hardware, but that's a decision for the customer to make using their specific and personal financial and capex / hardware information that you don't have at the end of the day.

Further, the gap between frontier open weight models and frontier proprietary models has been rapidly shrinking and continues to. See Kimi K2.5, Xiaomi MiMo v2, GLM 4.7, etc. Yes, Opus 4.5, Gemini 3 Pro, GPT-5.2-xhigh are remarkably good models and may beat these at the margin, but most work done via LLMs does not need the absolute best model; many people will opt for a model that gets 95% of the output quality of the absolute frontier model when it can be had for 1/20th the cost (or less).