A Local AI Runtime Is More Than a Model Loader

The true boundary of a runtime

A complete local AI runtime must at least handle the model lifecycle, context management, memory pressure, inference scheduling, tool calls, permission boundaries, and failure recovery.

A recommended layering

Application
  ↓
Agent Runtime
  ↓
Provider Abstraction
  ↓
Inference Runtime
  ↓
Hardware Backend

This layering lets the same agent switch among LocalEngine, Ollama, LM Studio, or cloud providers without binding product logic to a single inference framework.

Key metrics

• Time to First Token
• Decode tokens/s
• Peak resident memory
• Thermal throttling
• Cancellation latency
• Structured output success rate