What makes up a Compute instance?
A Compute instance is a unit of compute resources you run on Hivenet. Depending on your use case, an instance can run as a container or as a virtual machine. When you create an instance, you choose a combination of hardware, runtime type, and configuration that determines how it behaves and what level of control you have.Container-based instances
Containers are designed for most common workloads, such as running scripts, applications, and models.- Root access is disabled. Commands that require sudo won’t work.
- System-level packages are managed through templates rather than manual installation.
Virtual machine instances
Virtual machines are intended for workloads that need full operating system control.- Full OS-level access, including sudo and kernel-level configuration.
- Support for standard Linux distributions such as Ubuntu, Fedora, and Debian.
What you choose when creating an instance
Regardless of the instance type, you’ll configure the following:- GPU(s)
Choose from the GPU presets currently available in your selected location. Compute currently uses NVIDIA RTX 5090 GPUs, with 32 GB of VRAM per GPU and multi-GPU configurations available when capacity allows. - vCPUs and RAM
Review the CPU and system-memory resources attached to each preset. These support the runtime, data loading, preprocessing, and CPU-side parts of your workload. - Storage
Instances include fast local SSD storage. Storage behavior depends on the instance type and lifecycle. See the instance lifecycle documentation for details. - Region
Select where your instance runs. Region choice affects latency, capacity, and data residency. - Billing type
Instances use on-demand billing. Eligible usage is charged per second while the instance is running.
Preset specifications and availability can change as capacity changes. Use the creation screen as the source of truth for the hardware, memory, disk, bandwidth, and price you can launch now.
How to choose the right instance
The right choice depends on what you’re building.- Running scripts, apps, or model inference?
A container-based instance is usually the simplest option when a prepared image already includes what you need. - Running a GPU workload?
Start with the model or application’s VRAM requirement. One RTX 5090 provides 32 GB of VRAM. Add GPUs only when the software supports multi-GPU execution or when you want to run work in parallel. - Need full OS control, custom system packages, or kernel access?
Use a virtual machine instance. - Unsure whether you need GPU or CPU resources?
See Choosing between GPU and vCPU instances.
Multiple GPUs do not automatically behave like one GPU with combined VRAM. Frameworks and models need explicit support for approaches such as tensor parallelism or distributed training.
Managing your instance from the dashboard
Once your instance is running, you can manage it from the Compute dashboard:- Start or stop the instance
- Monitor usage, uptime, and cost
- Terminate the instance when you’re finished