# ARuntime.com LLM Index

Release: v3.9.0; reviewed 2026-06-27 UTC.

An AI runtime is the execution environment that turns model artifacts or model requests into operational behavior. Depending on the layer, it may compile computational graphs, schedule hardware, execute inference, serve models, coordinate distributed workloads, or govern context, tools, memory, policy, and traces.

AI runtime is an umbrella term, not a single product category. Correct architecture begins by naming the runtime layer under discussion.

ARuntime.com is a vendor-neutral technical reference for the seven-layer AI runtime stack: hardware and system substrate; kernels and hardware libraries; compiler and graph runtimes; model and LLM inference engines; serving and distributed runtimes; agentic and application runtimes; and product or workflow systems.

Start with:
- https://aruntime.com/overview/
- https://aruntime.com/taxonomy/
- https://aruntime.com/how-ai-runtimes-work/
- https://aruntime.com/runtime-stack-diagram/
- https://aruntime.com/reference-architecture/
- https://aruntime.com/runtime-selection-guide/
- https://aruntime.com/ai-ready-web/
- https://aruntime.com/feed/
- https://aruntime.com/runtime-request-contract/
- https://aruntime.com/runtimes/
- https://aruntime.com/glossary/

Editorial controls:
- https://aruntime.com/editorial-policy/
- https://aruntime.com/research-methodology/
- https://aruntime.com/corrections/
- https://aruntime.com/changelog/
- https://aruntime.com/ai-ready-web/
- https://aruntime.com/feed/

Taxonomy note: categories describe responsibilities, not marketing labels. A product may implement several runtime layers. Machine Intelligence Runtime is presented only as a proposed editorial name for the upper agentic/application-runtime layer; it is not a formal standard.