Go from zero to a running AI stack in minutes - pick a template, get MLOps out of the box, and run on fractional GPUs so you only pay for the slice you need.
QuickPods packages the tools most AI teams stitch together by hand - serving, fine-tuning, notebooks, experiment tracking, a model registry - into one-click templates on GPUs virtualized with our proprietary podvirt layer. No Dockerfiles, no cluster setup, no idle whole-GPU bills.
Launch an Unsloth/LoRA template, point it at your dataset, ship.
vLLM template with autoscaling in a click.
Reproducible research env with metrics and artifacts logged automatically.
Open QuickPods and choose a ready-made template - vLLM, Unsloth, ComfyUI, PyTorch and more - from the catalog. No CLI, no Dockerfiles.
Select how much GPU you need, from 25% to 100%, powered by podvirt - you only pay for the slice you use.
Click start and your pod boots in seconds into a ready-to-use Jupyter notebook with the template's software pre-installed. Start building right away.
QuickPods is PodStack’s one-click deployment product for production-ready AI stacks. You pick a template - vLLM, PyTorch, ComfyUI, Unsloth and more - and it launches on a fractional or full NVIDIA GPU with MLOps tooling already wired in.
QuickPods run on NVIDIA A10G 24GB, L40S 48GB, A100 40GB, A100 80GB and H100 80GB. Every GPU supports fractional allocation from 12.5% up to 100% through PodVirt, so you only pay for the slice you need.
Billing is per-second and scales linearly with the GPU fraction you allocate, and there are zero egress fees. Pricing is sales-led - contact sales@podstack.ai for a quote tailored to your workload.
QuickPods ship with experiment tracking, a model registry, real-time cost tracking and metrics logging, so you can go from template to a monitored deployment without stitching tools together.
Yes. QuickPods start from one-click templates, but you can launch any OCI-compatible Docker image. The templates are a fast path, not a limitation.
No. PodStack’s control plane, scheduler and PodVirt virtualisation layer are proprietary and purpose-built for fractional GPU sharing - not OpenStack and not vanilla Kubernetes. That is what makes 12.5% GPU allocation and sub-second scaling possible.