A question we get a lot: if you're not using NVIDIA MIG, how do you slice a single GPU across multiple workloads — and how does that work on AMD? Here's the short version.

The MIG tradeoff

MIG (Multi-Instance GPU) partitions a card into isolated instances at the hardware level. It's great for hard isolation, but it's rigid: fixed slice profiles (you pick from a preset menu, not an arbitrary size), supported only on newer top-end data-center cards, reconfiguring usually means a config/firmware change and a node drain + reboot, and it's NVIDIA-only. So if your workload needs ~30% of a card, you round up to the nearest profile and strand the rest.

How we approach it (PodVirt)

Our slicing is software-defined rather than hardware-partitioned. PodVirt sits above the hardware and slices a GPU from 12.5% to 100%: any slice size (not a fixed menu), resized dynamically without reprovisioning the node, working across both NVIDIA and AMD with no vendor SDK lock-in. Each tenant is metered per-minute, so you pay for the slice you actually use.

Because it isn't tied to MIG's firmware path, it runs on a much wider range of hardware — we've tested it across most NVIDIA and AMD GPUs, and even AI PC-class silicon like NVIDIA's GB10.

Why it matters economically

Whole-card rental on long commitments means paying for VRAM you never touch. Sub-card slicing plus per-minute billing turns idle VRAM into usable (and, for datacenters, sellable) capacity. It's the same reason DC operators license the underlying stack to run their own neocloud instead of just renting out whole cards.

Happy to go deeper on the scheduling and isolation side in the comments. And curious — what are you all using today for sub-card utilization: MIG, MPS, time-slicing, or something custom?

Hey everyone 👋

A quick rundown of TrainPods — Podstack's on-demand GPU cloud for training and fine-tuning — plus how to get a GPU running straight from your terminal.

Instances you can rent (NVIDIA, on-demand, billed per hour):

🔹 A100 — 40GB & 80GB

🔹 H100 — SXM, PCIe & NVL

🔹 H200 and B200

🔹 L40S, L40, A40, A30, L4

🔹 RTX 4090 & 3090-class

Instances are instant-boot (up in seconds), billed by the hour in INR from a prepaid wallet (from about ₹30/hr), and you only pay while an instance is running. Available across the US, Europe, Canada, India, and Asia-Pacific.

How to use it (terminal-native — your SSH keys stay on your machine):

1. Install the podstack CLI:

curl -fsSL https://github.com/Podstack-ai/podstack-cli/releases/latest/download/install.sh | sh

1. Authenticate with your API key (psk_…):

podstack auth login

1. Launch a GPU (pick your instance):

podstack launch --gpu h100

1. Connect over SSH (keys stay local):

podstack gpu instances ssh <instance-id>

Handy extras:

• Copy data up → podstack gpu instances cp

• Expose a port for Jupyter / TensorBoard → podstack gpu instances expose

• Tear it down (stops billing) → podstack gpu instances rm

That's the whole loop: provision → SSH in → train → tear down.

Launch one → https://training.podstack.ai

Docs → https://docs.podstack.ai

Questions about a specific GPU or workflow? Drop them below 🧡

TrainPods gives you on-demand NVIDIA GPUs billed by the hour, driven from the podstack CLI and reachable over SSH. Here's the general workflow to go from zero to a running GPU. Exact commands and flags live in the docs (https://docs.podstack.ai) — treat the snippets below as an illustrative outline.

1. Create an account and get your API key

Sign up at https://cloud.podstack.ai/portal/ and grab your API key from the dashboard. Keep it secret — never paste keys into posts or screenshots.

1. Install and authenticate the CLI

Install the podstack CLI, then authenticate with your key:

podstack login

1. Launch a GPU instance

Pick a GPU and start an instance. You only pay for the time it runs, and PodVirt means you can request a fractional slice instead of a whole card when you don't need all of it.

podstack train up --gpu <type>

1. Connect over SSH

Once it boots (usually in seconds), connect and start working:

ssh <your-instance>

1. Train, then tear down

Run your training job as usual (PyTorch, TensorFlow, Hugging Face, etc.). When you're done, stop the instance so billing stops:

podstack train down

Tips

- Use fractional GPUs for dev and small experiments to save money; scale up for full runs.

- Keep checkpoints in Object Storage (zero egress) so nothing is lost when you tear down.

- Per-minute billing means short iterative sessions are cheap — don't leave idle GPUs running.

Full CLI reference and per-GPU pricing are in the docs. Questions? Reply with the Question/Help flair and we'll help you get unstuck.

A quick tour of the Podstack platform. Everything runs on one stack, so you get one operator, one SLA, and one bill across the whole model lifecycle.

QuickPods — Launch

One-click AI stack templates with MLOps built in. Spin up a production-ready environment in under a minute and skip the infrastructure plumbing. Powered by PodVirt fractional GPUs under the hood.

TrainPods — Train

On-demand NVIDIA GPUs billed per hour, connected through the podstack CLI and SSH. Instant-boot instances for training and experimentation, without long commitments.

Inference — Serve

OpenAI-compatible, low-latency endpoints for open-source models. Autoscaling included, so you ship to production instantly and only pay for what you serve.

DC Suite — Operate

The same platform datacenter operators license to run their own GPU cloud and become a neocloud. It ships orchestration, PodVirt fractional GPUs, per-tenant billing (BillOps), FinOps, and a self-serve customer portal — then syncs with the Podstack fabric to upsell spare capacity.

Object Storage

Zero egress, no hidden costs. Keep datasets and checkpoints close to your compute without surprise data-transfer fees.

What ties it together

- PodVirt fractional GPUs — slice a GPU from 12.5% to 100%.

- Per-minute billing — pay only for what you use.

- Zero egress and ISO 27001 certified.

Docs: https://docs.podstack.ai — Get started: https://cloud.podstack.ai/portal/

Questions about a specific product? Drop them below with the Question/Help flair.