Compute
Compute Overview
Deploy GPU and CPU instances directly from your DEVUP AI dashboard — full root access via SSH and Jupyter, per-second billing, and persistent storage options.
DEVUP AI Compute lets you launch cloud instances on demand. Choose between GPU instances for machine learning, training, and GPU-accelerated workloads, or CPU instances for general-purpose compute, scripting, and lightweight tasks.
GPU vs CPU — when to use which
| GPU Instances | CPU Instances | |
|---|---|---|
| Best for | ML training, fine-tuning, inference, rendering, GPU-accelerated compute | Data processing, scripting, web scraping, CI/CD, general-purpose workloads |
| Hardware | NVIDIA GPUs (RTX, A-series, H-series) with dedicated VRAM | Multi-core vCPUs with configurable RAM |
| Storage | Container Disk + Volume Disk (ephemeral) or Network Volumes (persistent) | Container Disk (ephemeral) or Network Volumes (persistent) |
| Billing | Per-second while running, reduced idle rate while paused | Per-second while running, reduced idle rate while paused |
| Access | SSH + JupyterLab | SSH + JupyterLab |
Resource sizing guide
When selecting an instance size for Large Language Models (LLMs), a general rule of thumb is ~2GB of VRAM per billion parameters for unquantized models. For example:
- 7B models: ~14GB+ VRAM
- 13B models: ~26GB+ VRAM
- 70B models: ~140GB+ VRAM (or ~35GB+ when using 4-bit quantization)
Use tools like the Hugging Face Model Memory Calculator to estimate exact requirements.
| Workload | Recommended Tier |
|---|---|
| LLM Inference | Mid-range to High-end GPU |
| Training / Fine-Tuning | High-end GPU (multi-GPU for large models) |
| Image Generation & 3D Rendering | Entry to Mid-range GPU |
| Computer Vision | Mid-range GPU |
| Data Processing & Scripting | CPU Instances |
Availability depends on your selected data center. See the GPU Deploy and CPU Deploy pages for current options.
How it works
- Choose a tier — select a GPU model or CPU flavor from the live catalog in your dashboard. Availability and pricing are shown in real time.
- Configure your instance — pick a container template, set your storage allocation, and optionally attach a persistent Network Volume.
- Deploy — your instance provisions in seconds. Once running, connect via SSH or JupyterLab.
- Manage — pause to save costs (a reduced idle rate applies), resume when ready, or terminate to stop billing entirely.
Prerequisites
- A DEVUP AI account with a funded balance (top up from Billing).
- For SSH access: an SSH public key saved in Account Settings → Security.