Local AI can keep prompts and files on hardware you control, but it shifts model downloads, updates, performance, and security to you. These tools cover desktop model runners, inference servers, visual workflows, and self-hosted application frameworks.
Desktop runner or production server
A desktop runner is the simplest path for trying local language models. A production inference server is designed for concurrency, batching, monitoring, and API access. A node-based image workflow or application framework solves a different layer of the stack.
Match the model to the hardware
Model size, quantization, context length, image resolution, and concurrent users determine memory and compute needs. Start with a small representative model and task before buying hardware or building around a throughput estimate.
Local does not automatically mean secure
You still need access controls, software updates, encrypted storage, backups, network boundaries, and safe model or plugin sources. Confirm licenses before redistributing a model or using its output commercially.
Questions to ask before choosing
Does local AI work without the internet?
Many local runtimes can execute downloaded models offline. Initial downloads, updates, integrations, or external search features may still require a connection.
Do I need a GPU for local AI?
Not always. Small or quantized models can run on a CPU, but a compatible GPU usually improves speed for larger language, image, audio, and video workloads.