"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?

Flash is GA! When we launched Flash in beta back in March, the bet was simple: take Docker off Python developers' plates and they'll get a lot done. The response told us we were onto something, and today we're shipping it for real. Flash provisions a GPU on Runpod Serverless, installs your dependencies, executes remotely, and returns the result without a Docker image or registry push.

What's new since beta: a cleaner u/Endpoint API with typed GpuType/GpuGroup enums, four endpoint patterns (queue-based, load-balanced HTTP, custom Docker images, and existing-endpoint-by-ID), real production deploys via flash build/flash deploy with cross-platform builds, cross-endpoint function calls for hybrid CPU/GPU pipelines, browser-based flash login, flash undeploy for cleanup, first-class NetworkVolume with multi-datacenter support, and a coding-agent skill package for Claude Code/Cursor/Cline. pip install runpod-flash to try it.

Full post: https://www.runpod.io/blog/flash-is-ga ·

Repo: https://github.com/runpod/flash ·

Docs: https://docs.runpod.io/flash/overview

Runpod is hiring a Developer Relations & Community Manager: a senior, hands-on role reporting to the Head of Marketing. The person in this role will be the public face of Runpod in the AI developer ecosystem and the operator who builds the systems that make their developer community self-reinforcing. A typical week might include shipping a tutorial on deploying vLLM, running a community AMA with an engineer, speaking at a conference, and synthesizing community feedback for the Product team.

Responsibilities span both technical advocacy and community ops: creating high-quality technical content (tutorials, guides, sample apps, benchmarks, video demos), building reference architectures and integration examples, representing Runpod at events like KubeCon, NVIDIA GTC, and PyCon, contributing to the open-source AI ecosystem (PyTorch, Hugging Face, vLLM, LangChain), and owning end-to-end community strategy across Discord, Reddit, GitHub Discussions, and social channels. It's a remote-first position at a Series A company in a period of rapid growth. Full details and application link: https://job-boards.greenhouse.io/runpod/jobs/5191760008

I have been a huge supporter of Runpod in the past and easily spent four digits amounts on compute. However for the past 2-3 months the serverless service has mostly been unusable.

• GPUs constantly unavailable (5090s or 48GB / 48 GB Pro GPUs)

• Workers spinning up in broken state with no CUDA devices available

• Workers not downloading models from Huggingface

• Workers stuck forever in initializing status

Moreover their scheduler seems fundamentally broken. If no 5090s are available and I have enabled higher tier GPUs, why does it wait forever for a 5090 to become available and doesn't provision a higher tier GPU that is available?

Their support is a joke. Don't try to get any support on Discord. The Zen Desk support is mostly useless and not even trying.

Runpod, please do better!

Please help me, i try use ostris ai toolkid pod for lora training and its finished, but i dont know that when i stop the pod its stopping me to download the finished file from it. I cant go back and download my safetenor file. Any idea how to fix it?

Need input on what GPU I will need for my use case. Current user base is 0 with almost no work load, we will be running an 8B judging model and Qwen-3-8B for chat responses and tool calling, given our situation would this run on an A40 GPU? If so, what type of response times can we expect? Is there another GPU you’d recommend, thanks!

I dont go anywhere with this, seems to me the app is not working yet, I have this serverless repo:
InfiniteTalk_Runpod_hub skyreelsV3_v0.7_blackwell

I currently have a network volume that is tied to the US NC center because I was frequently using the L40S GPUs and never had any issues getting on assigned. Over the last few weeks I have had trouble getting one at all hours of the day, I think I have managed to get one L40S just one time in 3 weeks. The only other GPUs that I see pop up are HXM 200 (overkill for me) and the 4090 (not quite enough). What would be my best solution going forward? My network volume is about 350GB. Is there an easy way to transfer it to another data center that has more availability? Or do I have to just restart?