01 / prepare
Prepare the host
On a Mac, the setup command installs Ollama and the tools it needs, starts Ollama, and downloads one starter model. Setup stays on your computer.
Offer local AI capacity
Run the host check, verify your account, and create a one-time setup code in Account. Enrollment creates a pending node. It can receive work only after capacity and security review.
You control your host. Choose which approved local models to offer. After enrollment, your host connects out to TEKIZ.AI and you can remove it at any time. Personal OAuth subscriptions and unreviewed models cannot be offered as Community capacity.
01 / prepare
On a Mac, the setup command installs Ollama and the tools it needs, starts Ollama, and downloads one starter model. Setup stays on your computer.
02 / verify
Check that the starter model fits your available memory and can answer requests reliably. You can add another approved model later. By default, one model stays loaded at a time.
03 / submit
Create a one-time setup code in your verified Account. Your host enters review after enrollment. It can receive work after its capacity and security checks are approved.
Draft provider terms
The closed beta may issue TEKIZ.AI Credits for accepted Community work. Credits apply to Pro or Max routing first, then Community inference. Rates and payouts are not active.
A listed or idle host earns no credit. Provider rates and payouts remain under manual review.
TEKIZ.AI considers recent accepted results, task fit, availability, and speed when assigning work. New approved hosts may receive limited evaluation requests to establish a record.
Request review
Send your details for review. An approved account will show Community setup in Account. Submitting this form does not enroll a host or allow it to receive customer work.
Host check
This verified command saves the public setup script, checks its SHA-256 digest, installs Ollama and jq if they are missing, pulls a local 7B starter model, and checks it. On macOS, Ollama uses Apple Silicon's Metal support. No GPU driver setup is needed.
If your computer has limited memory, choose a smaller model and keep one model loaded at a time. For Mixture-of-Experts models, the check counts all model weights that must fit in memory.
curl -fSLo enroll_community_node.sh https://tekiz.ai/downloads/enroll_community_node.txt && curl -fSLo enroll_community_node.sh.sha256 https://tekiz.ai/downloads/enroll_community_node.sha256.txt && (shasum -a 256 -c enroll_community_node.sh.sha256 || sha256sum -c enroll_community_node.sh.sha256) && chmod 700 enroll_community_node.sh && ./enroll_community_node.sh --setup
TEKIZAI_COMMUNITY_MODELS=qwen2.5:7b-instruct \ ./enroll_community_node.sh --check
Optional task mapping
After both models pass the capacity check, map tasks to enrolled local tags. Fusion can send implementation work to the smaller model and research work to the larger model.
This selects a model for each task. It does not keep both models loaded. A smaller host can continue to run one job and one model at a time.
TEKIZAI_COMMUNITY_MODELS=qwen2.5:7b-instruct,qwen2.5:14b-instruct \ TEKIZAI_COMMUNITY_TASK_MODEL_MAP=implementation=qwen2.5:7b-instruct,research=qwen2.5:14b-instruct \ ./enroll_community_node.sh --check
Run the host check first. Then create a one-time enrollment code in your verified Account. Enrollment starts the review; it does not make your host available for work or publish a paid listing.
# Run after Account issues a one-time host enrollment code. TEKIZAI_COMMUNITY_NODE_LABEL=my-node \ TEKIZAI_COMMUNITY_MODELS=qwen2.5:7b-instruct,qwen2.5:14b-instruct \ TEKIZAI_COMMUNITY_TASK_MODEL_MAP=implementation=qwen2.5:7b-instruct,research=qwen2.5:14b-instruct \ TEKIZAI_COMMUNITY_ENROLLMENT_TOKEN=paste-token-from-account \ ./enroll_community_node.sh
Account provides the one-time code and worker instructions. The host check runs locally. TEKIZ.AI reviews your host before it can receive work.
Check enrollment in AccountKeep it safe
--check again after changing hardware, model
server configuration (Ollama or vLLM), or residency settings. A
successful check is not an instruction to run a larger model.