Diagram: your apps send requests to Saylek, which serves three models from four machines: two of yours, one Kara shares with you and one Jason shares with you. Each request runs on one machine.
Pool your AI compute.
Combine capacity across machines, people, and locations.
Your apps
- Coding agentsClaude Code, OpenCode
- Personal agentsOpenClaw, Hermes Agent
- Automationsn8n, Activepieces
- AI appsOpen WebUI, your own
Saylek
api.saylek.com/v1
qwen3.8:27b, 2 machines, 1 shared with you
3 models · 4 machines
- desk-4090Serving Claude Code
- kara-5090Kara · Reachable
- mac-miniYou · Idle, ready
- mac-studioJason · Reachable
Pool your AI compute.
Combine capacity across machines, people, and locations.
All the models served by each machine you add become accessible through one centralized Saylek API endpoint.
Put every GPU to work.
Dynamically route workloads wherever capacity is available.
Beyond exposing capacity across multiple machines as one, Saylek lets you rank your workloads by priority without the inefficiency of dedicating hardware to them. Give higher-priority applications precedence when requests are waiting for capacity.
Share what you’re not using.
Let others use your idle capacity. Your work has priority.
You choose when and who you share your AI compute with. Sharing means people can request inference from the models your machines serve. Nothing else on the machine is shared or reachable. You stay in control: your work has priority, and you can pause or remove anyone you share with, or limit sharing to specific hours or days of the week.
Use what others aren’t using.
Tap into trusted compute beyond the hardware you own.
No machine of your own? Ask someone to share
Start with what you have.
Use your machines
Pool the machines you already own into one endpoint, with priority for your own work. Share them with people you trust when you choose to.
Add a machineUse machines shared with you
Use the models people share with you from the tools and places where you work.
Ask someone to shareInvited? Open the link in your invitation email, or sign in with that address.
Questions
Make more of the AI compute you own, and the compute people share with you.
Do I need a GPU to use Saylek?
No. You can use the models running on machines other people have shared with you.
If you do have a capable machine, connecting it makes its models reachable from the tools and places you work, and lets you share it later, if you choose to.
Which models can I use?
The models already running on your own machines, and the models on machines other people have shared with you.
No model runtime yet? On a machine with an NVIDIA GPU, or a Mac with Apple Silicon, Saylek can set up one starter model, and downloads it only when you say yes.
Which tools can I use?
Tools that speak the OpenAI-compatible API or the Anthropic Messages API.
What happens when no machine can serve my request?
You are told that no eligible machine is available. Saylek doesn’t quietly answer from capacity you didn’t approve.
What stays under my control when I share?
You choose when and who you share with, and what you share is inference from the models your machines serve, and nothing else on the machine. Your work has priority, and you can pause or remove anyone you share with, or limit sharing to specific hours or days of the week.
What happens when I need my GPU and someone else is using it?
Your work has priority on your own machine over requests from the people you share with. You can also turn off sharing for that machine at any time, and new requests from them stop reaching it.
Does sharing expose my machine to the internet?
Connecting a machine doesn’t ask you to open a port on it or hand out an address. The machine connects out to Saylek, and shared requests arrive over that connection.
Is this renting GPUs from strangers?
No. Saylek connects machines you own and machines shared with you, with access managed through personal sharing or your organization. It doesn’t rent out GPU capacity.