This task can be performed using Lium.io
Agent-first GPU cloud for renters and providers
Connect an Agent to GPUs
Start by choosing a rented NVIDIA GPU with enough VRAM for the agent's model and tools. Install the provider SDK, create scoped credentials, and confirm your agent-first compute workflow can submit, inspect, and stop a remote workload.
Use the SDK to request the GPU, wait for a ready state, then send the model or inference command defined by your agent runtime. Validate one small task, capture the workload identifier, and release the rental in both success and error paths.
Best product for this task
Lium.io
cloud
Agent-first GPU cloud. Rent NVIDIA GPUs via web, CLI, SDK, or API. Providers earn 95% rental revenue + bonuses.
What to expect from an ideal product
- Scoped SDK authentication and credential rotation
- GPU discovery by model and VRAM
- Reliable create, status, and stop controls
- Actionable logs, errors, and readiness states
- Transparent pricing and cleanup safeguards
More about readiness checks
Most connection failures occur after allocation but before inference. Treat readiness as a separate checkpoint, not proof that the model process, network path, or credentials are usable.
- Poll SDK status until the workload is ready, with a timeout and clear failure path.
- Run minimal inference before connecting the agent to the rest of your ML infrastructure.
- Stop failed or idle rentals in a finally block or equivalent cleanup handler.
