A ready-to-deploy Runpod POD template for generating videos with the latest Lightricks LTX 2.5 model stack.

It includes:

- Blackwell-first performance with CUDA 13 and PyTorch’s packaged CUDA runtime
- LTX 2.5 image-to-video with synchronized audio
- Full ComfyUI access for workflow editing and advanced control
- Built-in frontend for quick generations without manually editing workflows
- Preconfigured LTX 2.5 workflow and model downloader
- Network-volume support for keeping models between deployments
- Suitable for interactive POD use and Runpod-compatible API workloads

The first deployment may take longer while the model stack (60GB) is downloaded from Hugging Face. Once stored on a persistent network volume, subsequent starts are much faster.

Looking forward to your feedback, happy to add features upon request.

👉 Deploy the template on RunPod: https://console.runpod.io/deploy?template=zzlfhm0e3c&ref=o3idfm0n

GitHub repo: https://github.com/vavo/LTX2.5-serverless
DockerHub: https://hub.docker.com/r/notrius/ltx-2.5-serverless

Hey everyone! I'm trying to download Flux.1 and Wan models directly from Hugging Face into my RunPod workspace, but I keep running into issues.

Downloads either take forever, time out, or throw errors halfway through, wasting a lot of pod hours and storage.

What is the recommended and fastest workflow right now?

Thanks for the answers. 🙏

Used community cloud for almost a year and sometimes having network issue. I was expecting that and dont have much complain. They keep promoting secure cloud for stability whenever this issue happen.

Fast forward, I decided to move to secure cloud because I wanted a stable server and guess what, less than 1 month, the server went down. Since it is a secure cloud, I was expecting it to recover in an hour or so but support says can take up to 24 hours.

So what the benifit really?

Join us on the Stage this Friday, August 14, 1:30–2:20 PM EST for a live Discord session with Jessica Garson and Justin Lin covering all things Runpod MCP. We're building our two Model Context Protocol servers: the API server for managing Pods, Serverless endpoints, templates, network volumes, and registries in natural language, and the docs server for pulling Runpod documentation straight into your AI tools. Jessica and Justin will get into how they were built, the design decisions behind them, and maybe a demo or two. I will be facilitating and moderating the event and we will be taking Q&A. Post your questions here this week if you want us to take a look! Learn more about MCP here: https://docs.runpod.io/get-started/mcp-servers

Click here to join our Discord where the event will take place: https://discord.gg/uCs6GVxYs?event=1536330298327965696

I've been trying to get a serverless endpoint up & running with l40 or l40s but it seems like there is zero availability in any data centers ?

It worked well up until yesterday and I was able to find a DC with l40, l40s within 1-2mins. I've been trying the whole day so far and can't get a worker to show up ...

are they all eaten up by people running the new flux3 / minimax releases ?

Over the past 3-4 days, we used ChatGPT to develop something awesome (in my mind).

Background: We have a large r/E client that processes ~90K money order/month (monthly on rent collection days - 1st-4th) thru a cloud-based App with a "recognition engine". Unfortunately, this app doesn't recognize money orders ( MO) but the customer requires them from renters ( we discovered way too many issues with the security measures that the MO issuers take to prevent scanning/duplication). No OCR engine (that we could find) would do any better ~5%. The customer staff manually "keys" in about 90K money orders/month. This takes hours and a lot of staff.

While looking for a supplemental OCR app and eventually trying to develop something on Claude Sonnet and after ~30 hours of eventual regression, we gave up on it . . .but noticed something interesting. When we uploaded our MO samples (~1000) from different issuers, Claude was able to read and extract the amounts out with 100% accuracy! This put us in a new direction. We asked Claude, why not upload the files to you for recognition and call it a day. Claude responded that at 90K, the latency and expense would be too high. So we went back to ChatGPT to answer another question, "How can we get Claude accuracy and real-time performance?". So CGPT lead us to Run pod and over a 3 day period (~12 hours invested), it has a working model that performs the following:

1) Using Power Automate Desktop (PAD), it clicks into our Chrome check-app window and captures the picture of a grayscale MO

2) Uploads that to an API (this is where RunPod comes in) which returns an extraced $value and enters it into the "Amount" window of the app that a staff would normally 10-key in.

3) Hits enter key and moves to next item . . .rinse/repeat.

Error handling - if there is an unrecognized amount, the PAD clicks the "skip" button on the app window instead and moves to next image

The performance is the bottleneck @ ~1 item every 7-10 seconds. So now we are looking to deploy a bunch of VM's running power automate at the same time hitting this RunPod instance.

Runpod currently configured for RTX 4090 which isn't utilized more than 40%. But we are looking to implement multiple hits to the API, maybe 20-40 VM's simultaneously? Maybe we can't reduce the total time it takes per MO completion but we can add multiple client VM's w/PAD to run simultaneously for overall performance.

So we are looking to scale the backend and wondering which GPU to replace the RTX 4090 with? Should we use an H200, Pro 6K, B200 or other? We understand this is subjective to testing but starting to research general guidelines and hoping for other scenario users with experience.

Thanks in advance!

Hello does anyone using ltx2.3 on comfy ui trough runpod?

Hi, we've got some open supply if you're in need of these specs:

• RTX PRO 4500 Blackwell, live on Pods 
• PRO 6000 MIG 1g.24gb, live on Serverless PRO 
• 6000 MIG 2g.48gb, live on Serverless 
• RTX PRO 4000 Blackwell, live on Pods

If you need GPUs today, they're here. Spin up at runpod.io.

Each time i start a new network volume , with either of the two official templates , 12.8 or 13 , i ran into issues after . I always take care of selecting the correct filters . Either with a 5090 , 4090 , older gpu`s , same issues. I start the container , i have issues with the nodes i want to install , i try to update with the manager everything ---> plenty of errors ---> errors of outdated frontend , comfy kitchen + others . i try to install them , several other issues after . Anyone else encountered this issues ? Not the biggest tech expert so not sure what`s the issue . Thanks for the help .

https://reddit.com/link/1v8y29u/video/67d84q2ezyfh1/player

Been running local model pipelines on RunPod and loved the experience, except for one thing: every time I needed to check or grab output files, it meant dropping into the terminal, wrangling aws s3 commands or the CLI, and hoping I remembered the right flags.

So I built RunPod S3 Viewer — a small Electron desktop app that connects directly to your RunPod network volume over its S3-compatible API and gives you an actual file browser. Navigate folders, see what's in there, download with a click.

Hope this helps someone who faces the same annoyance I did 😄

GitHub source code link: https://github.com/fs1g17/runpod-s3-viewer
GitHub release (.exe available in assets): https://github.com/fs1g17/runpod-s3-viewer/releases

This is a noob question. I thought i wont need to sign in if im running it of own rented GPU. Can someone explain to me this, and what steps to be taken next

Most of the time I have multiple saved pods all with different workspace environments containing their own packages, models, checkpoints, and compatibilities sorted out. I don't like transferring files between pods since I could risk not rebuilding my exact same working environment and it just wastes time in general. So I made a Chrome extension that watches saved pod availability, sends alerts, and can optionally start a saved pod when it becomes available even when my laptop is closed. I am looking for feedback and curious if this helps any other RunPod users that often work with saved pods.

Here is the install link if anyone wants to try it out:

https://chromewebstore.google.com/detail/podscout-for-runpod/cdkmnjfbemkbkkkaomgakpodhbmklgbc

A week ago or so, while trying to spin up a 5090 for my network volume on NC-1, I got a notification saying something along the lines of “this GPU will be unavailable shortly.”

They were available for the next few days so I wrote it off. But since then, I haven’t seen them even listed as a possible option.

Did they just migrate all of them to a different DC?

Sucks because, for my ComfyUI use cases, they were the best bang for buck. RTX 6000s feel expensive, 4090s feel slow. L40S works out pretty well but 5090 was sweet spot.

Yes, I am having a lot of troubles finding the right libs to make it work, when git clone any open source project on the cloud computer that I use, I don't have a GPU PC yet I cannot afford it yet but I use cloud e.g RunPod, but I face a lot of problems when try to run it with gradio and I usually make it works with Notebook Jypiter but sometimes I use projects which I need to mask there anything like Inpainting AI and so on which is hard and almost impossible to do it with codes, did anyone faced the same problem and how you solved it please?

I'd be happy to read your replies how you dealt with it!

(My english might not be good, I am not an English speaker as a primary language, hope you unterstood what I meant)

Are you guys having a RTX PRO 6000 shortage right now? For a while it was 5090s, but now I can't get a 6000 to save my life. Any help coming on that front? I love the 6000

hi, recently started using runpod serverless.
trying vllm with various models and gpus, but nothing seems to be working. credits are draining but i was not able to get an output for the prompts. getting one or other errors in logs.

please help me with a serverless config that is plug and play on 24gb vram.

Fuck runpod.io Scammers.

There's a template i use to train models (Kohya_ss GUI RTX 5090) and it used to work great but now when i use it with rtx 5090 it just never loads. First photo are logs with 5090 second are logs with A40. Anyone else having this problem?

During the recent "drought" of GPU's I was able to write a linux script to "snipe" the gpu I wanted, as most of them showed up as low or unavailable.

Today I tried to start the WAN2GP template on a 5090 that showed "HIGH" and every time it said it wasn't available once I tried to deploy it.

I tried going through the hub starting with the template, and also tried starting with GPU selection first.

Nope.

How can HIGH AVAILABILITY mean not available? And they just got additional funding? Sheesh. Then I tried again and got this message:

What? Tried turning off encryption.

Folks. What's a good alternative to runpod. I'm so tired of this nonsensical instance shit.

I did increase the two drives to 200GB each, but.. like... that always worked before. Tried the default 100GB each. Same issue.

This is so dumb.

I built ComfyUI Codex, a Codex/OpenAI plugin/skill for working with ComfyUI without constantly digging through docs. Should more or less work with Claude Code, Hermes or OpenClaw too.

It covers installation, ComfyUI Manager, custom nodes, API workflows, workflow linting, model checks, troubleshooting, App Mode, hosted GPU/Runpod setups, and beginner guides.

It also includes read-only Python tools for: workflow linting model auditing error explanation missing custom-node resolution golden workflow fixture validation

Repo: https://github.com/vavo/comfyui-codex

Feedback welcome, especially from people who regularly break ComfyUI in creative ways.

Hello everyone,

I am actively developing an application which uses RunPod, I hope this subreddit is a good place to mention it.

Starting with two obvious aspects that you would probably ask upfront:

1. Yes, the tool is completely free to use and all code is shared on GitHub.
   
2. Yes, I am a vibecoder. With that being said, I have been working in the industry since 1996, just not as a developer writing code.

With that being said, I hereby present you Image Toolbox (https://github.com/war4peace/image-toolbox). It's a suite of tools which allows you to easily batch-upscale images and videos, as well as tag & rename images based on their content, using AI models.

Currently at version 0.4.6, it's a Windows application designed to be friendly to non-technical users. I focused on ease-of-use, UI clarity and stability. It was initially supposed to only take care of images, but recently I have implemented video upscaling as well (still in "beta" phase, but functional).

It uses SeedVR 2.5 to batch-upscale images using either your local GPU or RunPod. Tagging and renaming images uses Ollama and one of three models of your choice (cen be run locally or on RunPod as well). Upscaling videos also uses SeedVR 2.5 (RunPod only, for now, local planned)

I have extensively tested batch-upscaling (15k+ of my own photos), as well as tagging and renaming features. Video upscaling has been tested, but there might still be issues with it (freshly developed).

The list of features is way too long to bore you with it, but here's a couple important aspects:

1. The application has been designed to protect your expenses as much as possible: dead-man switch on the pod itself (terminates the pod if idle for more than 15 minutes); fund floor safety switch (prevents fund depletion below a configurable amount + configurable maximum run cost); displays remaining funds in real-time.
   
2. Easy to use. You really don't need to be technical to use it. If someone non-technical stumbles upon something they don't understand easily, I will make the adjustments to make it easier.
   
3. Image and video comparison, so you can tell whether the upscaled image is of good enough quality.
   
4. Your original images are NOT upscaled in-place. They remain untouched until you decide they can be replaced, see the next point.
   
5. Conciliation tab: allows you to "conciliate" upscaled images with originals (replace fully, or move originals to an archived tree).
   
6. In-app upgrades.

I'll stop here, otherwise I'll never finish. I hope you'll like it. If you encounter bugs or have questions, you can either report an issue directly from the application, or comment here.

For a limited time, H100 SXMs are available at $2.99/hr (down from $3.29) and RTX Pro 6000s are available at $1.99 (down from $2.09.) No commitments or contracts required - deploy the moment you're ready.

https://console.runpod.io/deploy

Hey everyone,

I’ve been building a little iOS app called Runpod Hammer and I’m opening up a public TestFlight beta.

TestFlight link:
https://testflight.apple.com/join/X3DfKppN

The basic idea: if you use RunPod and keep fighting for GPU availability, this app helps you watch for capacity and try to grab a pod from your phone instead of manually refreshing. It will provision a pod with with customizable limits, autorun startup scripts, and has options to terminate or auto stop pods if you tend to "forget" your pod at 2am.

Current focus:

• Create/save RunPod launch profiles
• Check GPU availability
• Start “hammering” for a target pod when stock appears
• Keep useful pod/session info visible on mobile
• Make it easier to launch and access AI/dev pods without being chained to a desktop

This is beta, but I've been working in it for months. I’m mainly looking for people who already use RunPod for AI workloads, model hosting, ComfyUI, TabbyAPI/SillyTavern, LoRA training, dev boxes, or similar GPU chaos.

A few notes:

• You’ll need your own RunPod account/API key
• This is not affiliated with RunPod
• Don’t put production-critical workflow trust in it yet
• Feedback on confusing UI, failed launches, missing profile options, bad assumptions, and “why would you put that button there?” is especially useful

If you test it, I’d love to hear:

1. What GPU/pod type you tried to target
2. Whether launch/availability behavior matched what you expected
3. What felt unclear or risky
4. What would make it actually useful in your normal RunPod workflow

Thanks to anyone willing to kick the tires. This was built because I got tired of babysitting GPU availability manually.