The worker app is a Windows service with a tray UI. You keep control of the limits;
the scheduler handles placement.
01
Check
Run the local check. It measures your hardware and estimates a tier. Nothing is
uploaded until you request an invite.
02
Install
After an invite, install the worker app and confirm the model and backend it
should serve.
03
Set limits
Choose a VRAM budget, model-cache quota, idle-only wait, and active hours. Jobs
outside them are refused on your machine; CPU, power, thermal, and network ceilings
are planned.
Limits and failure handling are part of the design.
The worker is a guest on a contributor's PC. The scheduler plans around limits,
heartbeats, and clean failure.
Limits are enforced locally.
The enable switch, VRAM budget, model-cache quota, idle-only wait, and active hours
are checked on the worker machine. A job outside those limits is refused; CPU,
power, thermal, and network ceilings are planned.
No prompt logging by default.
EasyAI does not log prompt text by default. The assigned worker sees the prompt
and intermediate inference data to run the job.
Failed workers replan.
If a worker drops, the scheduler retries inside a bounded plan — at most two
attempts — then fails the request cleanly.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://<your-domain>/v1",
api_key=os.environ["EASYAI_API_KEY"],
)
stream = client.chat.completions.create(
model="lfm3",
messages=[{"role": "user", "content": "Explain leases in one paragraph."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")