AI and GPU Labs for Training Cohorts
Run hands-on AI training where each learner has their own GPU and a ready JupyterLab. We connect neo-cloud GPU providers to your cohort lifecycle and add auto-shutdown and cost dashboards, so a forgotten instance does not burn the budget.
- GPU labs
- AI labs
- JupyterLab
- Fine-tuning workshops
- Neo-cloud GPUs
- Auto-shutdown
Who it is for
- AI training programs
- Fine-tuning workshops
- Vendor partner enablement for AI products
Typical buyer: AI training programs, fine-tuning workshops, vendor partner enablement.
What ships
- Per-learner GPU provisioning across neo-cloud providers
- JupyterLab / VS Code Server with notebooks pre-loaded
- Cost dashboards so finance sees what cohort #2 will run you
- Auto-shutdown so a forgotten H100 doesn't burn the budget
Timeline. Six to eight weeks for AI and GPU programs that involve neo-cloud provisioning.
Pricing. Contact us for pricing.
Common questions
Which GPU providers do you use?+
We work with neo-cloud providers such as Lambda Labs, Shadeform and RunPod. We use a hyperscaler when your IT team requires it for security or compliance.
What is installed in the lab?+
PyTorch, vLLM, Ollama and CUDA, with your notebooks loaded in JupyterLab or VS Code Server.
How do you control GPU cost?+
Every lab has auto-shutdown, and a cost dashboard shows what the next cohort will run you.
Scope your lab in 30 minutes
Bring a rough idea of the course, product or cohort size. We will tell you whether to hire us, use the free pre-built labs, or buy a lab platform.
Book a discovery callOr send us details
We reply within one business day.