Always-on GPU capacity for models that stay loaded
Keep your model ready for the next request without loading it from scratch each time.
HIPS is developing hourly capacity for inference workloads that need a model to remain resident. This supply will come from providers suited to continuous availability, primarily data centers.
We are inviting early-access interest while we develop the platform.
Pay for capacity while you need it
The hourly charge covers allocated capacity, including time between requests. Compare compatible GPU configurations and the applicable quote before allocating resources.
The Console will show allocation state, model loading, readiness, accumulated usage, and the controls to stop the deployment. A GPU being allocated and a model being ready to serve are separate steps.
Choose around the model
GPU memory, model architecture, runtime requirements, and expected workload all affect the right configuration. Supported region selection will be available through the higher paid plan.
Keeping weights loaded removes repeated model-loading work. Response time still depends on the model, hardware, input, and workload.
Plan for steady work
Always-on capacity is intended for custom models and other inference workloads where continuous allocation makes sense. It is a distinct option from the shared pool of idle personal GPUs.