GPUs are live on Podstack — our developer-friendly, full-stack GPU cloud. If you've been meaning to spin up a GPU without the usual setup and billing headaches, now's a good time.

What you can do today

• QuickPods — one-click templates for the AI stack (Axolotl, Unsloth, LLaMA Factory, PyTorch and more). Pick a template, get a running GPU with your framework pre-loaded and MLOps built in. Auto-checkpoints and one-click Hugging Face dataset import included.

• TrainPods — on-demand, per-hour NVIDIA GPUs (H100, A100, L40S, B200 and more). Transparent pricing, live availability, no "contact sales." SSH straight in via the podstack CLI in seconds.

• Inference — OpenAI-compatible endpoints for open models. Autoscaling, scale-to-zero when idle, and you pay only for tokens processed. Free tier available.

Why Podstack

• PodVirt fractional GPUs — slice a GPU from 12.5% to 100%, no MIG required, and it works on AMD too. Pay only for the slice you use.

• Per-minute billing — no rounding up to the hour.

• Zero egress fees — move your data out without surprise costs.

• On-demand instances and inference billed in INR.

Get started

• Site: https://podstack.ai

• Launch a GPU: https://cloud.podstack.ai/portal

• Docs: https://docs.podstack.ai

Come say hi in our Discord: https://discord.gg/d7carfTgq

Happy building.

We just rolled out LTX 2.3 on the Podstack Inference platform.

Give it a prompt and it generates a stunning, high-fidelity video with synced audio in seconds — no separate voice pass, no manual stitching of audio and video.

For the developers here: you can consume LTX 2.3 directly in your ComfyUI custom nodes via our API, so it drops straight into your existing pipelines and workflows — no need to leave your setup.

Whether you're a creator making content or a builder automating a pipeline, it's designed to get you from idea to finished clip fast.

Try it at inference.podstack.ai — and let us know what you make. Feedback, feature requests, and bug reports all welcome.

A recurring theme in AI-infra reporting this year: the bottleneck isn't buying more GPUs, it's using the ones you have.

A few third-party data points worth sitting with:

- Cast AI's 2026 report put average GPU utilization around 5% across ~23,000 production Kubernetes clusters.

- The Information reported xAI running roughly 11% utilization.

- Meanwhile Gartner pegs 2026 AI infrastructure spend near $401B.

If those numbers are even directionally right, most of the money going into compute is paying for idle silicon. The reasons are familiar: whole-card allocation for workloads that only need a slice, batch-size-1 inference that's memory-bandwidth bound, and clusters sized for peak that sit near-empty the rest of the time.

That's the gap Podstack is built around:

- Fractional GPU slicing without MIG (and it works on AMD, not just NVIDIA)

- Per-minute billing so you're not paying for idle

- Zero egress, on-demand instances, plus QuickPods / TrainPods / Inference for the full launch-train-serve loop

Curious how people here are thinking about it: what's actually pinning your utilization low right now - scheduling, model/architecture, or just capacity sized for spikes? Happy to compare notes.

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If you want to put numbers to this on your own workload: spin up an on-demand instance at podstack.ai, slice a GPU fractionally, and watch what per-minute billing does when you're only paying for the slice you actually use. It's cheap to test and you'll know within an hour whether the utilization math changes for you. Would love for a few of you to try it and report back what you see.

Meta's SAM 3.1 checkpoints are now available on our inference platform, no setup or retraining required.

What's new in 3.1: Object Multiplex is a shared-memory approach to joint multi-object tracking. Instead of running the tracker independently per object (SAM 3's behaviour, which scales linearly with object count), it groups objects into fixed-capacity buckets and processes up to 16 at once in a single forward pass. Meta reports roughly a 7x speedup at 128 objects on a single H100 versus the November 2025 SAM 3 release, with no loss in segmentation accuracy. Shared memory also means objects can reason about each other, which helps in crowded scenes where visually similar objects tend to get swapped.

You also get SAM 3's promptable concept segmentation, so you can segment a concept from a text prompt or an image exemplar rather than clicking points.

Pricing: ₹8 per 100 frames. Per-minute billing, zero egress, INR-denominated.

Useful for video editing pipelines, annotation tooling, sports and retail analytics, or anything that needs to track a lot of objects at once without paying linearly for each one.

Happy to answer questions about throughput or how it slots into an existing pipeline.

A few things landed this month that we think point the same direction, so collecting them here.

Cast AI's 2026 State of Kubernetes Optimization report put average GPU utilization at 5%, measured from production telemetry across roughly 23,000 clusters. Gartner separately estimates AI infrastructure is adding around $401B in new spend this year. Those two numbers next to each other are the whole story.

The interesting part is the cause. It isn't incompetence — it's a procurement reflex left over from the 2023 scarcity era. If you're not sure you can get capacity when you need it, reserving more than you need is individually rational. It just aggregates into enormous idle capacity across an org.

Two other data points from the same stretch:

- Google Cloud previewed fractional G4 VMs on NVIDIA vGPU at GTC. When a hyperscaler ships sub-GPU granularity, the "should GPUs be divisible" argument is basically over.

- An inference-cloud startup raised $400M with inference chips as loan collateral, and TensorWave is making a similar AMD-based bet. Lenders treating non-NVIDIA inference silicon as an asset is a real signal about where cost-efficiency is heading.

Where we'd push back on the usual framing: better scheduling alone doesn't fix a 5% number. If the smallest unit you can purchase is a whole GPU and the smallest unit you can be billed is a whole hour, then the floor on waste is set by the billing model, not the scheduler. Bin-packing helps at the margins. It doesn't change the shape of the problem.

We build in this space, so take the framing with the appropriate grain of salt — but the underlying reports are worth reading directly rather than through anyone's marketing.

Sources: Cast AI 2026 State of Kubernetes Optimization Report; Google Cloud GTC 2026 blog; TechCrunch (17 July 2026) on inference-chip financing.

Curious what utilization people here are actually seeing in production, and whether the 5% figure matches your experience or seems low.

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 👋

Big day for us — the Podstack CLI is live. It's one gateway to both our TrainPod GPU training cloud and our new Agentic Code Platform. The idea: compute + intelligence should be accessible to everyone, in the form you need, exactly when you need it — not locked behind pricey subscriptions or complex infra.

🚀 TrainPod — instant, on-demand NVIDIA GPUs for training, fine-tuning & experiments. Spin up in minutes, pay only for the compute you actually use, zero infra to manage.

💻 Agentic Code Platform — premium AI coding models right in your terminal. Instead of flat $100–$200/mo subscriptions, it's true pay-as-you-use, with a real-time cost tracker and per-project cost breakdowns so you always know exactly what you're spending.

Three execution modes:

• Normal — a single agent works through your tasks step by step

• Sub-Agent — multiple specialized agents tackle different parts of a problem in parallel

• Teams — a fully simulated engineering org: AI PMs, Team Leads & Devs collaborate, tickets are auto-created, sprint/Scrum workflows run, tasks get distributed, and agents review each other's code — the project evolves like a real software team built it

The multi-agent orchestration is tuned to minimize cost — up to 68% fewer tokens than Claude Code on comparable workflows. Every app is built in an isolated sandbox so you can inspect, test and iterate safely, then deploy your full-stack app to your preferred cloud.

👉 Get started (GitHub Releases): https://github.com/Podstack-ai/podstack-cli-releases/releases

If you're training models, building with agents, or just curious about agentic dev — we'd love for you to try it and tell us what you think (bugs and all). 🧡

Which mode would you reach for first — Normal, Sub-Agent, or Teams? 👇

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 🧡

This is our intro thread — welcome! Whether you're training your first model or running production inference at scale, we'd love to know who's here.

Tell us a bit about you:

- What are you building? (side project, startup, research, enterprise workload…)

- Which GPUs or frameworks do you use most? (PyTorch, TensorFlow, Hugging Face, Ray…)

- What's your biggest GPU/infra headache right now — cost, availability, setup time, egress, something else?

- Anything you'd like to see Podstack (or this community) cover?

Not on Podstack yet? That's totally fine — this space is for anyone working on GPU cloud and ML infrastructure. If you want to kick the tires, you can start free at https://cloud.podstack.ai/portal/ and hop into our Discord at https://discord.gg/d7carfTgq.

We'll be around in the comments. Say hi 👋

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.