Your model with a clearer path to inference
Import a supported model from Hugging Face, choose an execution pool, and use the HIPS SDK to run inference across supported model types beyond language models. For compatible open-weight language models, use a supported provider-style client. Choose always-on GPU capacity when the model needs to remain loaded.
We are inviting early-access interest while we develop the platform.
General notes
You have trained a model for a reason. HIPS is being built to help you put it to work.
Choose where your model runs
Choose between personal GPUs and an approved-provider pool. Compare the quoted price, current availability, and model-access conditions before deploying.
Cross-pool fallback will be off by default. If you enable it, it must still respect your region and data-use rules.
Personal GPU supply is intended to provide a lower-cost option. The actual quote will depend on the model, eligible capacity, and the selected pricing mode.
Customer models will run in the approved-provider pool first. Personal GPUs will follow later, only for supported ONNX models and only for deployments you opt in. If you opt in, the device owner’s machine will hold your model’s weights while it serves your requests.
Keep your weights separate from request data
Your model weights will never be used to train other models, on any plan.
Request inputs and generated outputs are governed separately. On Free/PAYG and the lower paid plan, they may be used for training, including when requests run against your own model. On the higher paid plan, request-data training is off by default.
Execution-pool choice also matters. Before deployment, you will need to understand the model-access conditions of the selected pool. Encryption in transit and at rest does not by itself prevent a GPU operator from accessing data or weights while a workload runs.
Match capacity to the workload
A model used occasionally and a model serving steady traffic may need different deployment choices. HIPS will profile supported customer models before assigning a tariff, accounting for more than memory fit alone.
Always-on capacity keeps a model loaded between requests and is charged by allocated time. Usage-based access, where supported, follows the applicable model tariff.
Tell us what you need to run
Join early access with your model’s architecture, approximate size, task, and expected traffic. These details help us assess support without requiring you to upload model weights during enrollment.