Rent GPU cloud capacity

Choose a GPU rental path

Pacific serves teams that need compute for AI workloads. The Rent Compute link opens the Pacific Compute catalog on this same domain. It is the place to inspect current rental options and begin a rental flow. Catalog inventory and commercial terms should be checked there at the time you order, because they can change.

For a larger, predictable workload, Pacific also discusses reserved, single-tenant GPU capacity. The existing capacity offer is built around a complete B300 module with its own fabric and an agreed deployment plan. A reservation is a commercial and engineering conversation, with configuration and acceptance terms set for the particular project.

These paths serve different planning needs. Begin with the rental catalog when you want to evaluate rentable compute. Bring a forecast and site requirements to a capacity call when the workload calls for a dedicated cluster. Pacific can then discuss which path fits the scale and timing of the work.

H100 rental and newer GPUs

Searches for H100 rental often start with a model name. A useful comparison also needs the current catalog listing, the form of access, and the order terms. Check Pacific Compute for the GPUs it currently lists and for any H100 configuration offered there.

The same care applies to B200 GPU rental and B300 rental searches. Compare GPU count per node, the network available to the workload, and the capacity term. If your jobs span many GPUs, discuss topology, storage, software control, and acceptance criteria before choosing a commercial path.

Pacific's published reserved-capacity reference is a B300 module. It describes a single-tenant bare-metal cluster, while the rental catalog is the authority for rental products. This distinction matters when comparing a short experiment with a sustained training or inference program.

How GPU rental pricing works

GPU rental pricing depends on the hardware selected, the quantity and shape of the allocation, the term, and the services included. Networking, storage, support, and deployment requirements can also change the commercial discussion. A price for a single GPU cannot be assumed to represent the cost of a whole cluster.

On-demand rental and a reservation are different buying motions. A rental catalog can expose current order terms for a defined product. Reserved capacity asks both parties to agree on a configuration, duration, deployment setting, operating responsibilities, and acceptance evidence. Pacific discusses reserved-capacity commercial terms privately on a qualified call.

For a usable comparison, record the total workload size, expected run time, and dependencies such as data access and interconnect. Then compare offers on the same scope. Look to the current catalog or a written proposal for a price specific to the chosen product and term.

Ask how the unit of purchase is defined. A listed GPU, a full node, and a dedicated cluster can carry different commitments and supporting services. Include the expected length of each run and the time between runs when estimating total cost. For a reservation, confirm which site and acceptance responsibilities are included in the proposal so the comparison covers the complete deployment.

From rental to reserved capacity

A pilot can reveal the software stack, utilization pattern, and performance measures that a larger deployment needs. Those facts give Pacific and your team a better basis for discussing reserved capacity. It helps to identify which jobs must share a fabric, whether bare-metal control matters, and how you will judge handoff.

Pacific's B300 reference pairs dedicated compute with a configurable fabric and a factory, site, and integration test program. The company describes witnessed acceptance evidence for the reserved module. A final configuration and schedule are set through a site-specific review; illustrations on the website are design studies.

Some teams need compute while a permanent site is still being prepared. Pacific describes a bridge-capacity path for that situation. The bridge plan begins with verified power, site readiness, and the period that needs coverage. The same discovery can determine whether a direct reservation makes more sense.

Plan the infrastructure around the job

GPU choice is one input to a complete AI infrastructure plan. Cluster-scale work also depends on power, cooling, network topology, storage, and operating access. Pacific designs factory-produced modules and evaluates those interfaces with the customer before a reserved deployment proceeds.

For projects tied to a particular location, the site itself becomes part of the capacity decision. A prepared pad, usable power, and an agreed connection plan shape deployment. Read the AI factory sites guide for the site side of the equation, then bring your workload and site assumptions to a call.

Frequently asked questions