Local AI
Running AI models and agents on your own hardware. Privacy-first, offline-capable, fully under your control.
The case for local AI
Cloud AI is convenient, but it comes with trade-offs: latency, cost, privacy exposure, and vendor dependency. Local AI flips the equation — your models, your hardware, your data.
In 2026, local AI is no longer a hobbyist pursuit. Consumer hardware (Apple Silicon, modern GPUs) can run capable models, and the tooling has matured to make local deployment straightforward.
Key categories
Local model runners
Ollama — The simplest way to run LLMs locally llama.cpp — High-performance inference for GGUF models LM Studio — Desktop app for running local models with a GUI Jan — Open source ChatGPT alternative that runs locally
Local agent frameworks
OpenClaw — Full agent operations platform, local-first Open Interpreter — Natural language interface to your computer PrivateGPT — Chat with your documents, fully offline
On-device inference
MLX — Apple’s machine learning framework for Apple Silicon MLC LLM — Universal deployment for LLMs across devices ExecuTorch — PyTorch on-device inference
What to watch
- Apple Silicon enabling increasingly capable models on consumer laptops
- Quantization improvements making larger models practical locally
- Hybrid local/cloud architectures that route by task complexity
- Local agents that match cloud agent capability for common tasks