Plain-English explanation
Instead of typing a prompt into a website that sends it off to a server somewhere else, local generation means the model itself, its checkpoint or weights, is downloaded onto your own machine, and every prompt runs through it right there. Nothing about the request leaves your device unless you choose to share the output yourself.
This is the model behind tools like Stable Diffusion run through ComfyUI or Automatic1111, or a chatbot model run through something like text-generation-webui or llama.cpp-based software: you supply the hardware, the software, and the model file; the company that trained the model has no visibility into what you generate afterward.
How it works
You download an open-weight model, a checkpoint or a GGUF file, and run it through local inference software that uses your own GPU, or in some cases CPU, to compute each inference request rather than sending it over a network. Performance depends heavily on your own hardware, VRAM in particular for image and video generation, which is why quantization and formats like GGUF exist: to shrink models down to something that fits and runs acceptably on consumer GPUs.
Adult-AI use
Local generation keeps prompts and generated content on your own device rather than sending them to a third-party server's logs, which can matter for privacy around adult content. It is not gated by a platform's content policy or NSFW filter because the model runs on the user's hardware, including when that model is an uncensored model. The tradeoff is cost and maintenance: capable image and video generation needs a GPU with sufficient VRAM, setup and updates are the user's responsibility, and output quality is limited by the hardware and models available locally.
How it differs from hosted generation
Local generation trades convenience and access to the very largest models for privacy, cost control after the initial hardware investment, and freedom from a platform's content policy. Hosted generation trades that control for convenience, a lower upfront cost, and typically access to bigger, more capable models than most consumer hardware can run at all. See hosted generation for the other side of this comparison.
Common uses
- Running Stable Diffusion, Flux, or SDXL locally through ComfyUI or AUTOMATIC1111 for image generation.
- Running open-weight LLMs through llama.cpp-based tools, KoboldCpp, or text-generation-webui for uncensored companion chat.
- Running local video models on high-end consumer GPUs.
Limitations and misconceptions
- “Local” doesn't mean unlimited: you're still bounded by whatever the base model was trained to do and by your own hardware's speed and memory.
- Local generation isn't automatically safer or more legal than hosted generation: a platform's content policy is replaced by the laws applicable to what is generated and how it's used, which doesn't go away just because no company is monitoring it.
- Setup complexity is a real barrier: local tools generally assume comfort with installing software, managing model files, and troubleshooting, unlike a polished hosted app.