Get guidance on the right GPU class, runtime template and usage budget before you start spending.
Managed cloud GPUs for AI builders
Rent GPU power without waiting for hardware, forex headaches or cloud complexity.
GPU.info.na packages RunPod cloud GPUs with local onboarding, monthly billing support and practical AI deployment help for Namibian businesses, universities, agencies and EU teams.
Global GPU infrastructureDIC.net supportNamibia and EU ready
Why GPU.info.na
Cloud GPU access with practical help from a team you can talk to.
For Namibian teams, we help bridge the gap between AI ideas and working GPU environments with clear communication and support.
Move from experiments to managed inference endpoints, private AI tools and repeatable deployment workflows.
Best-fit customers
Who should buy this first?
AI agencies
Short GPU bursts for client demos, RAG pilots, agents, image/video generation and custom model tests.
Universities and labs
Temporary training/fine-tuning capacity without buying expensive local hardware.
Developers
Jupyter, PyTorch, ComfyUI, vLLM and Docker environments launched quickly.
SME automation
Private inference endpoints for document processing, support bots and workflow automation.
Media teams
GPU acceleration for image generation, video tests and batch creative workflows.
EU startups
Lean GPU capacity and hands-on deployment support before committing to large cloud contracts.
Service packages
Simple ways to start using cloud GPUs.
GPU.info.na adds setup guidance, usage planning, deployment help and ongoing support around cloud GPU workloads.
Starter GPU Desk
N$499 setup + usage
- One assisted GPU pod launch
- Template help: Jupyter, ComfyUI or PyTorch
- Usage estimate before spend
- WhatsApp/email handover
Managed AI Pod
N$1,950/mo + GPU usage
- Monthly billing/admin support
- Container/template setup
- Basic uptime and spend checks
- One support block included
Inference API Pilot
From N$6,500 build
- Serverless endpoint planning
- Docker worker packaging
- API key and logging setup
- Cost-control recommendations
Workload planning
We help match the workload to the right GPU class.
Instead of guessing, start with the model size, expected runtime, storage needs and whether the workload is interactive, batch-based or API-driven.
| Workload | Typical GPU class | Good fit |
|---|---|---|
| AI notebooks and prototyping | 24GB GPU | Jupyter, PyTorch, small model tests |
| Image generation and creative tools | 24GB-48GB GPU | ComfyUI, Stable Diffusion, batch generation |
| LLM inference pilots | 48GB-80GB GPU | vLLM, TGI, private assistants and RAG demos |
| Fine-tuning and heavier AI jobs | 80GB+ GPU | Larger models, longer runs and managed storage |
| Production inference APIs | Serverless or managed endpoint | Autoscaling, API keys, logs and cost controls |
How it works
From idea to running GPU workload.
1. Scope
Tell us what you want to run, the expected hours and whether it is a prototype or production workload.
2. Launch
We recommend the GPU class, runtime and budget, then help get the environment running.
3. Operate
For managed clients, we help monitor usage, refine deployments and package the workload into a repeatable service.
Quote request
Tell us the workload. We will recommend the GPU and monthly budget.
Use the form to generate a WhatsApp message with the details needed for a quick quote.