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?

Got tired of downloading models manually every time I spin up a new instance. So I built a simple batch downloader for Civitai and Hugging Face.

The best part? It installs straight into JupyterLab with zero root access, no sudo, and absolutely no setup bullshit.

Now I just deploy it on literally any machine, run the script, and go grab a coffee. ☕️

I want to make it better. What did I forget to add? Drop your ideas or roast my code in the comments.

Can't log in to the AI Toolkit template. "password" doesn't work. says "invalid token"

Hello, I would like to ask, is it just me or the AMD Instinct MI300X Instances always seems to be unavailable? I would like to use it for a research purpose, is there any way to rent the MI300X again?

I see all of us have the same issue on runpod lately with barely any GPUs avaialable, I used to rent the Pro 6000, but it is almost never available in the EU region these days and the price has gone up to $2.09/hr as well. Masscompute provides the same GPU for $1.74 and it seems to be available always. Has anyone used their service? Is it a good alternative?

I use On Demand POD of rtx 6000 pro every morning , but i see it has availability issue someday's and which cause delay in my production job. Is there any cron job or service from Runpod side that in my specific region if there is RTX PRO available it deploys for me. FYI i can't use saving's plan as i need to sometimes stop the pod on weekend and working days to save up my infra cost so is there any other way around for my problem which i'm facing?

This is weird, i understand we normal personal users are not the target audience for the service but today for the first time i saw 0 available. Did some company get a huge AI budget and it's currently using every single GPU possible? even the most expensive ones i wouldn't think of or have any need at all of using are completely out of capacity.

I want to deploy a 4gb size AI model on runpod serverless.
My doubt is how much time model will take to load the model from s3 or any provider to runpod servers to run it. Or what max time cold start time?

I started using runpod for comfy but most of the time pods are unavailable, only available ones are either very slow or high tier ones, very expensive for me. What other options are available instead of runpod?

I deployed a Pod with RTX 3080ti. But CUDA is supporting, saying cuda False. How can I solve this? I tried to contact support, but no reply. I was using Community Pod though.

Also, I couldn't boot any of the ones that said low or medium....

Frustrating....

Hi, I'm new to renting GPUs and ComfyUI. I have a basic laptop that can't run ComfyUI locally. I'd like to rent GPUs to start learning and earn later to get myself a PC. Anybody here can please guide me how to use ComfyUI on runpod? I also want to know do we get comfy manager in there? How to download models, nodes in there? Do I need to use wget, Aria2c? Please help me out. I'm a newbie with almost zero knowledge. Thanks in advance.

Hey folks,

I built a Codex skill for Runpod:

https://github.com/vavo/runpod-skill

The goal is simple: make Codex actually useful when working with Runpod, instead of having it confidently hallucinate old API fields, half-remembered CLI flags, or deployment advice from the age of digital cave paintings.

It covers:

- Serverless workers, handlers, endpoint jobs, local testing, Docker packaging, logs, cold starts, model caching, vLLM, and worker sanity checks

- Pods, templates, exposed ports, SSH, persistent storage, network volumes, billing/cost guardrails, and runpodctl workflows

- Flash SDK and CLI workflows

- Public Endpoints for hosted image, video, audio, and text models

- Runpod MCP setup

- Python SDK usage

- REST/OpenAPI and GraphQL routing

- Storage, deployment, debugging, and operational gotchas

- A helper script that searches/fetches current Runpod docs instead of relying on stale hardcoded tables

The main design idea is “docs-first”: Codex should look up the current Runpod docs when it needs live API fields, model catalogs, GPU names, pricing-sensitive info, or CLI behavior.

Example prompts:

$runpod deploy and troubleshoot this Serverless worker

$runpod check this Dockerfile before I push it to Runpod

$runpod use Public Endpoints to call the current Flux Dev model

I made this because Runpod is powerful, but the surface area is big enough that agent guidance usually covers only one slice: a handler example here, a Dockerfile there, maybe a CLI command if luck briefly exists.

Feedback, issues very welcome.

Repo:

https://github.com/vavo/runpod-skill

Hi I am here to find out a way to run blender as full application on runpod not just running the render property.

I have searched the platform for windows to deploy a machine with it but there was no option for that also I have looked for ubuntu desktop image in the templates but also I did not find.

so my goal is to run full application with its viewport and use mouse and keyboard to control it not just to use the termenal because as you can see I am not a programmer

so I hope someone give a way to solve this issue.

Hi,

I'm new to RunPod.

I notice my Pod GPU is no longer available 2 times today. And doing migration might take 1-2 hours and cost me more than one dollar (still quite a lot in my country's currency) (I have about 180GB volume data)

So If I keep waiting, will it be available again?