The OS Moment for LAS
Bring your Agent home.
For three years, we've all been renting AI by the token. Every prompt you typed, every document you uploaded, every agent you built — lived in someone else's data center.
Your Agent has been living at the office. Never home. Never yours.
That era is ending.
The 1981 IBM PC moment — for AI
In 2025, NVIDIA shipped the first $3,000 desktop AI machine: the DGX Spark. AMD, Apple, Dell, HP, and ASUS followed within months. Local hardware capable of running 200B+ parameter models — privately, without the cloud, without metering, without limits — is now available to anyone with a credit card.
The hardware came first. What comes next is the software stack: an operating system for the new machine. Apps that assume inference is free and local. Agents that can run for weeks without permission.
We call this new class of system a Local Agentic System — LAS.
SaaS was "software you don't own."
LAS is "AI you own."
AIoOS is the OS for LAS.
Our mission is to bring your Agent home.
A note on naming
"AIOS" has been used by research teams — notably agiresearch/AIOS from Prof. Yongfeng Zhang's group at Rutgers (COLM 2025) — to describe an LLM-agent kernel focused on scheduling, context management, and resource allocation. That work is pioneering and we respect it.
AIoOS is different. The lowercase "o" stands for onchain.
Where AIOS asks: "How do we run agents more efficiently?"
AIoOS asks:
"How do agents become sovereign?"
| agiresearch / AIOS | AIoOS | |
|---|---|---|
| Focus | LLM kernel, scheduling | Local + onchain + sovereign |
| Deployment | Cloud / on-prem | Your own hardware (DGX Spark, Strix Halo, Mac Studio) |
| Identity | — | HBGIS (DID + behavioral genome) |
| Persistence | Session-based | CapsuleAI + HSLTS (cross-lifetime) |
| Economic layer | — | Onchain native |
We stand on the shoulders of projects like agiresearch/AIOS, which pioneered the LLM-kernel abstraction. AIoOS extends that vision with onchain identity, local-first deployment, and cross-lifetime continuity.
Both visions are valid. This is ours.
Why LAS, Why Now
| Cloud AI (2022–2025) | LAS (2026→) | |
|---|---|---|
| Where AI runs | Someone else's GPUs | Your desktop |
| Billing | Per-token metering | One-time hardware |
| Your data | Uploaded, cached, logged | Never leaves the box |
| Agent lifetime | Killed at session end | Runs for days, weeks, months |
| Control plane | Provider's ToS | Your machine |
| Fine-tuning | Rare, expensive | Routine, free |
| Compliance | Opaque | Auditable — local |
Who LAS Is For
- 🏢 Founders shipping AI products who can't afford per-token billing at scale
- 🏥 Regulated industries (healthcare, legal, finance) where data must stay local
- 🛠️ Developers who want to own their dev stack end-to-end
- 🌏 Teams outside the US/EU working around API rate limits and sanctions
- 🧪 Researchers who need reproducible, offline inference
The First LAS App
Our reference implementation is live now:
👉 nemo3super
Zero-config private RAG chatbot running on NVIDIA Nemotron 3 Super 120B. Drop files, chat with them, data stays local. 10 releases in 24 hours.
One-click Start.bat launcher · WeChat-style UI · 10 supported file formats ·
MIT licensed · shipped entirely through Claude Code CLI.
What's Next
Keep reading:
- Four Pillars — How AIoOS structures Memory, Identity, Continuity, Autonomy
- First LAS App — nemo3super deep dive
- Roadmap — What ships next
Or jump straight in: