GPU Hourly Rental
Elastic compute, billed by the hour
Elastic compute, billed by the hour
Rent GPU capacity on demand, monthly, or annually, covering domestic and mainstream multi-generation GPUs. Scale smoothly from a single card for validation to a thousand-card training cluster — and never pay for idle compute.
from
1hour
Minimum rental
↑85%
GPU utilization
↓35%
Annual compute cost
Core capabilities
What GPU Hourly Rental solves
The problem with building your own cluster is buying for the peak and using it at the average. Hourly rental turns a fixed cost into a variable one, while cross-architecture scheduling pushes utilization from 30–50% to 85%.
Hourly elasticity
Scale out for a training or simulation peak and release immediately after — pay for what you use.
Matched by workload
We match card type to workload characteristics: large-memory and interconnect-sensitive jobs get H100 clusters with high-speed storage.
Ready environment
Pre-configured CUDA, frameworks, and a shared storage layer, so jobs can start as soon as the environment is provisioned.
Usage transparency
Per-job usage and cost are recorded and exportable, so internal cost allocation does not rely on estimates.
- Billed by the hour, starting from one hour
- H100 / A100 / domestic accelerators available
- High-speed storage and interconnect ready out of the box
Instance types
Three billing models for three workload shapes
Deciding whether your workload is short-term, always-on, or peak-driven — and billing accordingly — is the single most effective cost lever.
On-demand instances
Billed hourly
- Best for
- Model validation, short experiments, bursty inference
- Notes
- Start and stop at will, delivered within minutes. Suited to teams still validating, who should not prepay for an uncertain timeline.
Reserved instances
Monthly or annual
- Best for
- Steady training jobs, always-on inference services
- Notes
- Reserved capacity with tiered discounts over longer terms. Suited to predictable production workloads with stability requirements.
Cluster instances
Quoted per project
- Best for
- Large-scale training, CAE / CFD simulation
- Notes
- Multi-node fabric with distributed storage and high-speed interconnect, scaled elastically for the project and released when it ends.
Specifications
What surrounds the compute matters just as much
For large-model training and simulation, the bottleneck is often storage throughput, interconnect bandwidth, and data isolation policy rather than the chip itself.
- Accelerators
- Domestic and mainstream multi-generation GPUs
- Minimum rental
- 1 hour
- Delivery
- Minutes for on-demand instances; agreed schedule for clusters
- Storage & network
- Distributed storage with high-speed interconnect
- Data isolation
- Isolated instance networks with independently mounted volumes
- Data erasure
- Wiped according to policy on release, with customer-managed keys supported
Need a specific card type or a mixed deployment? Tell us in the enquiry and we will provide an actionable compute list with a measured baseline.
How to work with us
Four steps from first contact to live
A standard commercial process. Technical material and integration documentation are provided on request once we start working together.
- 01
Confirm workload characteristics
Tell us the framework, model size, memory, and interconnect requirements, and we match the best card type and topology.
- 02
Provision compute
On-demand instances are delivered within minutes; clusters are networked and mounted on the agreed schedule.
- 03
Submit jobs
Mainstream training frameworks and container images are supported, and the same job description runs across chip architectures.
- 04
Scale and release
Scale with your business rhythm, release instances when a job finishes, and storage is wiped according to policy.
FAQ
What buyers ask most
What is the minimum rental period?
Which accelerator models are available?
Is data wiped after the rental ends?
Explore the other product lines
The three business lines combine freely and share one account, one metering system, and one bill.
Data basisPerformance and cost figures on this page come from production statistics on the DengCloud platform and have been reviewed with our product and engineering teams.
Get a compute plan and quote
Tell us your workload characteristics and timeline, and our team will recommend card types with a cost estimate.
Explore platform capabilitiesBusiness response, Mon–Fri 9:00–18:00 (CST)