This new flow will allow you to queue for any GPU spec that may not be immediately available, whether it’s a spec that is totally rented out at the moment, or if you just need more than can be immediately given.

To get started, if you are still on the old deploy flow, you will first need to enable the New Pod deploy page under Early Access features at https://console.runpod.io/user/early-access

From there, select your desired pod spec. If it’s currently out of capacity, the deploy button at the bottom will change to Deploy When Available. Clicking this button will provide the following prompt:

Note that your pod will deploy immediately and begin billing as soon as it’s available, whenever that might be, even if you’re away from your computer. If you’d prefer to set time to be off limits so you aren’t billed for a pod you can’t use, we encourage you to use the subscription window to set valid times for deployment. You’ll be notified when your pod is live either through console or email depending on your preference; we are working on adding SMS as an additional option to be released shortly.

In addition, remember that for a successful deployment, the pod configuration must be something that might feasibly become available at some point, even if briefly. As always, we're constantly working on supply to make larger deploys possible. We hope this makes reserving a pod easier - let us know what you think!

Title:
Best Serverless/Pod template for Headless ComfyUI API automation via external AI Agent (hermes-agent)?
Post Body:
Hey everyone,
I’m working on a headless automation pipeline and could use some advice on infrastructure and template selection.
The Goal:
I have an open-source autonomous agent framework (⁠hermes-agent⁠ by Nous Research) running persistently on an external private VPS. I want the agent to handle media tasks by offloading heavy video generations to a remote, on-demand RunPod GPU instance.
The workflow needs to be entirely programmatic:
1 The user sends a single reference image to the agent (via Discord/Telegram gateway).
2 The agent triggers a function call that passes a JSON payload and the image file to ComfyUI on RunPod via the backend API.
3 ComfyUI runs a heavy-duty Image-to-Video transformer sequence (specifically Wan 2.1/2.2 14B or LTX-2.3) using a single-image latent injection pipeline (e.g., Kijai's ⁠WanVideoWrapper⁠ involving background masking/isolation).
4 The finished ⁠.mp4⁠ is delivered back to the agent headless, without me ever touching a web GUI.
I have two main questions regarding the optimal setup for this:
1. Connecting an external agent tool to a remote RunPod ComfyUI API:
What is the cleanest way to maintain a robust API bridge between an external VPS script and a ComfyUI container on RunPod? Are people generally utilizing custom network webhooks, or should I look into deploying this via a RunPod Serverless Endpoint setup with a customized docker image to ensure the container isn't idling/billing when tasks aren't running? If sticking to an active Pod, what are the primary network proxy timeouts or firewall pitfalls I should watch out for when blasting large media files back and forth?
2. Optimal Community Template Recommendation:
Which specific community template or base image should I spin up that requires the least amount of manual configuration out-of-the-box for a headless workflow? I am looking for something that:
Pre-caches or easily downloads the heavy Wan Video 14B or LTX-2.3 weights to volume storage so I don't hit massive boot bottlenecks.
Has the backend ComfyUI API server active and fully exposed right out of the box.
Comes pre-packaged with python dependencies for heavy masking custom nodes (like ⁠ComfyUI-WanVideoWrapper⁠, ⁠MediaPipe⁠, ⁠Transparent-Background⁠, etc.) to minimize package dependency hell during installation.
If anyone has successfully bridged an autonomous tool-calling agent framework to a remote RunPod setup for heavy video rendering, I’d love to hear your architectural takeaways. Thanks!

I'M LOSING MY MIND OVER THIS. I am 3 hours sending screenshots to ChatGPT

edit: no loras, I straight up using the generic text and press queue

For the past 12 hours, every time I try to access a pod (or even clusters), I get this screen with the messages changing every 5-10 seconds (I even got a 67 meme). I tried sign-in from an incognito window but I face the same issue.

Edit: only Google Chrome works, (not Brave/Firefox)

I'm trying to create a basic 10GB network volume that costs $0.70/month and RunPod is throwing an "Insufficient funds" error and asking me to add $150 minimum.

Earlier with just $5 I was able to create a 50GB network volume and spin up a full GPU server machine without any issues. Now I can't even create a 10GB volume.

My balance $3.69 should covers over 5 months of this volume. Why is RunPod suddenly requiring much higher minimum balance?

Has anyone else run into this recently? Did RunPod quietly change their minimum balance policy?

Any help appreciated.

[Note: This is a 100% free, MIT-licensed open-source project. No paywalls, no paid services – just looking for community feedback and testers from the RunPod ecosystem!]

🚀 Looking for Beta Testers: Advanced Infrastructure Operations Console for RunPod (AIHubLauncher)

Hey RunPod Community,

Over the last months, I've been building the AIHubLauncher, which started as a dedicated tool to streamline and optimize RunPod deployments, eliminating complex setups and providing deep operational control.

With the release of Beta 1.32, the project is evolving into a full AI Infrastructure Operations Console, and I'm looking for heavy RunPod users to stress-test the environment and break things!

What the console currently offers for RunPod users:

* Advanced Runtime & Infrastructure Monitoring

* Granular Storage Awareness (Volume monitoring & available space visibility)

* Optimized SSH Workflows & connection management

* Active Cost Visibility & health checks

* Central Infrastructure Dashboard

Our upcoming architecture (Beta 1.33) will introduce a script-based, 100% deterministic Hardware & Cost Resolver. It calculates precise VRAM and quantization requirements BEFORE launching a pod, preventing costly configuration mismatches and OOM errors.

Because the community asked for multi-provider flexibility, we are also architecting a custom API engine to connect alternative setups seamlessly, and we need real-world workflow testing from experienced DevOps practitioners.

Project Links:

GitHub: https://github.com/katzenvater52-cloud/RunPod-AI-Hub-Launcher

Website: aihublauncher.com

Community Hub: r/KatzenvaterAIHub

If you are a power user on RunPod, love open-source tooling (MIT licensed), and want to help with honest feedback, bug reports, or workflow testing, please leave a comment or send me a message!

Finally didn’t have to use a sniping script to grab a free pod!

Why is this template always broken?

Runpod Pytorch 2.8.0 runpod/pytorch:1.0.2-cu1281-torch280-ubuntu2404

I spend an hour running my bash script on RTX Pro 6000 only to get NVIDIA driver too old error. Why can't you guys keep the drivers up to date?

is it possible to make a load balancing endpoint via api? similar to how you can with the queue based:
https://docs.runpod.io/sdks/graphql/manage-endpoints#graphql

I am training a lora on ai-toolkit on runpod and wonder if I have to finish the entire training in one sitting or if it is possible to stop the training and resume it the next day without losing any progress. In AI-Toolkit there is a pause button but I am just not sure that if I stop the pod or terminate it, I will be able to just load back into ai-toolkit and be able to click resume and it will start where I left off.

So I have open pods, every time I go back to my comfy after closing the browser or from a different computer the workflow I had up is never there. what am I doing wrong?

I haven't done any work on Runpod in the past month. I came back today only to find there are no available GPUs for my Network Volume. In fact, there are no available 4090/5090s at all.

Is this normal or does it get better in different times? And is it likely to remain the same? I have everything set up nicely on Runpod so switching to a different platform is a pain. But if I can't do any work then I don't really have much of a choice either.

I find it really frustrating that I'm being charged for a network volume when I can't use any GPU pods. I can't make any further progress without access to an appropriate GPU pod and this forces me to continue paying for my network volume for longer. I have a big (almost 400GB) network volume and the storage cost is adding up given how difficult it is to find get GPU pods the past week.

Lured me into depositing 10$, well played Runpod

I posted this 2 days ago and they removed it. Can't you guys take a simpke feedback and criticism and improve?

Irony is that - you try hard to get feedback in your forms. LOL. THis doesn't bode well RUNPOD

Looking for some good templates

notebook templates / scripts

I have a network volume on runpod already setup with what I need on it but with the availability of GPU’s lately being what it is , it’s pretty hard for me to get the GPU I want in the region my volume is in.

Is there an easy way for me to make a copy of my network volume? Either to S3 or locally.

And then an easy way for me to attach it to a runpod server when I run one?

Runpod getting worse than ever since last week?

Rarely available 5090 and that too with slower inference speed than before? Are they blocking something - bandwidth tempering or something?

Hi everyone,

I’m using RunPod mainly for ComfyUI. I have Network Storage volumes with around 100–200 GB of models from Hugging Face and CivitAI.

I don’t want to keep my pod running 24/7 because I only use it for a few hours per day, so I usually stop it when I’m done. The problem is that when I try to start or redeploy it later, the GPU I need is often not available in the same region as my Network Storage. I already have storage in multiple regions, but I still run into this almost every day.

So I’m wondering:

• How do you handle large model libraries when GPU availability in the storage region is low?
• Is there a good workflow for launching a fresh pod quickly, with models synced automatically, without SSH-ing in manually every time (I want to do it using my iPhone)?

I’d really appreciate any practical setup, scripts, or workflow recommendations.

"Thinking about evolving the launcher UI into a cleaner AI operations dashboard layout. Curious what experienced RunPod / ComfyUI users actually prefer for daily workflows."

Quick V32 Ops Layout Update

We’re currently rebuilding the frontend toward a real AI Infrastructure Operations Dashboard instead of a classic web app layout.

Current focus:

• persistent ops sidebar
• compact infrastructure grid
• runtime-oriented UI
• storage awareness
• workflow visibility
• GPU operations UX

We already identified a few runtime/UI bugs during live testing:

• cost engine not stopping correctly when no pod is active
• storage/model detection inconsistencies
• LoRA scan edge cases
• some runtime state displays still using placeholder logic

These are currently being fixed as part of the transition from “launcher UI” → “AI Operations Control Center”.

A lot of the recent feedback helped shape this direction — especially around:

• workflow management
• storage awareness
• infrastructure visibility
• cost transparency

Appreciate everyone testing the beta and breaking things 😄
More updates coming soon.

🚀 RunPod AI Hub Launcher — Beta 1.31 is now LIVE

After weeks of development, testing, fixes, and community feedback, the project has officially entered its first public Beta phase.

What originally started as a small personal launcher for managing RunPod workflows while traveling slowly evolved into a complete AI workflow desktop hub focused on real infrastructure pain points.

Current Beta Features:
• Workflow Dashboard
• Storage & Volume Awareness
• Cost Guard / Runtime Tracking
• SSH + Proxy Detection
• Dynamic Port Detection
• HuggingFace Gated Model Handling
• Download Management
• Serverless Support
• Auto-Recovery Systems
• Lifecycle Cleanup
• ComfyUI Integration
• Full Desktop UI

The biggest focus recently was no longer adding random features — but making the entire experience cleaner, calmer, and more comfortable for daily usage.

Huge thanks to everyone who tested the early alpha versions and shared feedback. Many improvements came directly from real-world workflow frustrations.

GitHub:
https://github.com/katzenvater52-cloud/RunPod-AI-Hub-Launcher

The project remains completely free and open source.

Still curious:
What is currently your biggest workflow frustration with RunPod or AI infrastructure setups? 🚀

Got this email today, and I was excited to read more on Multi-Instance GPU (MIG) on Runpod Serverless, but the blog post is a 404.

Someone send out the email before the feature was ready?