On-Device LLM Inference Cheat Sheet

On-device LLM inference brings large language model capabilities directly to local hardware—laptops, desktops, mobile devices, and edge systems—without requiring cloud connectivity or API calls. This approach delivers zero-latency response, true privacy (data never leaves the device), and cost elimination (no per-token charges), making it essential for privacy-sensitive applications, offline environments, and cost-conscious deployments. The 2026 landscape centers on three core pillars: quantization (reducing model precision to fit consumer hardware), specialized frameworks (Ollama, llama.cpp, MLX), and hardware acceleration (dedicated NPUs, Apple Silicon unified memory, GPU offloading). Understanding quantization formats like GGUF, choosing the right inference runtime, and matching model size to available VRAM determines whether local inference runs at 5 tokens/second or 50.