Local vs Cloud AI: Architecture Tradeoffs | Jarvis Glossary

What is the difference between local and cloud AI?

Local AI describes any AI tool whose model inference runs on the end user's own device — laptop, desktop, phone, or on-premises server — rather than on a remote provider's infrastructure. The model file lives on local disk, runs against local hardware (CPU, GPU, Neural Engine, NPU), and produces output without network round-trips. Cloud AI sends prompts to a remote API (Anthropic, OpenAI, Google, Mistral) and receives responses. Local AI wins on privacy (data never leaves the device), latency (no network), offline capability, and zero per-token cost. Cloud AI wins on capability — no model that runs on a laptop in 2026 matches Claude Opus 4.5, GPT-5, or Gemini 2.5 Pro on hard tasks. Common local AI tools include Ollama, LM Studio, MLX, llama.cpp, and Apple Intelligence. Desktop assistants like Jarvis (getjarvis.eu) use cloud frontier models but encrypt the data path with AES-256-GCM and EU data residency (data at rest). Scroll down for the trade-off matrix.