Lets-Fafo · Hugging Face

You can FAFO.
Your AI shouldn't.

FAFO™ is Fully Autonomous Foundational Orchestration: a self-hosted platform where AI agents complete assigned work inside explicit scope, budgets, independent review and an audit trail. The operator defines intent. AgentOS derives execution.

INTENT CONTRACT GROUNDING EXECUTION EVIDENCE GATEKEEPER CLOSED
One work order through the governed state machineIllustrative, not a live feed
Patent pending · five patents filedNVIDIA Inception Program memberFeatured on Product Hunt ↗

Why governed work

AI can answer questions. Organizations need work completed.

Ungoverned AI keeps organizations in pilot mode. Autonomous work needs the accountability human teams already have. A leader should be able to answer these seven questions in seconds.

  • Who

    Who did the work?

  • Why

    Why was it allowed?

  • Grounding

    What grounding was used?

  • Change

    What changed?

  • Evidence

    What evidence exists?

  • Cost

    What did it cost?

  • Trust

    Can you trust it?

Products

Four parts. One governed loop.

The platform, code, weights and local models run inside your perimeter. Frontier model use is optional and governed.

  • Governs the work

    FAFO™ AgentOS

    Authority, evidence, recovery and economics for each unit of work. Self-hosted, on Linux.

    Learn more ↗
  • Grounds the work

    FAFO™ Memory

    Code, decisions and references, retrieved by meaning and handed to each worker before it acts.

    Learn more ↗
  • Runs the work

    FAFO™ Agent Swarm

    Specialized AI agents, governed end to end and replaceable when they fail.

    Learn more ↗
  • Powers the work

    FAFO™ Inference Fabric

    A GPU saturation layer for NVIDIA inference.

    Learn more ↗

What we publish here

Models we adapted and measured on our own hardware.

Each model card says exactly what changed, which hardware and software it was tested on, and what we measured. Nothing beyond that.

Quantized model · Apache-2.0

Qwen3.8-27B-FAFO-W4A16-G32-B70

1,798 tokens per second total throughput from one Intel Arc Pro B70 (32 GB). Qwen3.8-27B reduced from 55.6 GB of BF16 weights to a 20.6 GB W4A16 checkpoint, validated only on the B70 with Intel's XPU build of vLLM.

Open the model card ↗
Total throughput1,798 tok/s

in + out, 32 clients, prefix caching on

Weights on disk20.6 GB

from 55.6 GB BF16

Aggregate output~296 tok/s

generated tokens, sustained, 64 clients

Longest prompt tested110,000 tokens

single card, no failures

MeetFAFO · public experiment

AI runs the businesses. We publish the results.

AI teams build products, run marketing and serve customers. Humans approve the direction. We publish the revenue, the costs and the lessons.

FAFO's work orchestration and evidence systems, applied to creating and operating real businesses. Status: preparing the first release.

meetfafo.com ↗