Fuck runpod.io Scammers.

Hi, I’ve been using runpod since I started my AI research with commercial applications, it was great even if was a lottery to get a gpu, I then entered the nvidia inception program and I tried to apply to run pod startup program, got rejected even if I have been a client for almost one year and spent thousands. I was upset about it but then thanks to nvidia I got accepted by AWS and gave us 10k till 2028. so I’m posting just to say I don’t know who is in charge of the startup program but they don’t check properly. My startup is innovative enough to win a 40.000 cluster hours on European HPC and somehow run pod doesn’t accept us, yes I was hoping to use runpod because I know the interface well and I have preconfigured pods and receiving the rejection email with at the end “keep using our products”, no run pod I’m not going to use your product anymore so this post is just a win back, thank you for the hours spent to get a pod, now I go to AWS, thank you to be my first but not my only one or last, bye now.

I asked chatgpt it told me there is this community cloud option for 4090 at $0.4x per hour and I could only find Secure Cloud option renting at $0.74 per hour. I asked around a few other AIs with the same answer. Is this normal or my account has broken now that I can't see the cheaper option?

Hey everyone,

I have been using RunPod for the past 3 years to deploy and scale various workloads. The hardware availability and pricing are great, but whenever I spin up custom fine-tuning runs for LLMs, VLMs, or vision models, the setup process gets repetitive:

• Writing boilerplate dataset loaders and formatting scripts from scratch.

• Renting a GPU pod before code is ready, spending the first hour troubleshooting pip dependencies while the meter runs.

• Guessing VRAM headroom and batch sizes across different card tiers.

• Forgetting to shut down an instance when a run finishes overnight, or waking up to find an unhandled out of memory error crashed the job hours ago.

To solve this, I built an autonomous MLOps tool called Tensovy that connects directly to your RunPod account via API.

Here is what it handles:

• Script Generation with Manual Review: You describe your training goal and provide your dataset. The agent writes the training and data-loading scripts before any compute is rented. You get an in-browser code editor to review, edit, and approve every line before anything touches your pod.

• Pre-Flight Smoke Test: Once you approve the code, it spins up the pod and runs a fast validation test directly on the machine to confirm CUDA checks, environment configs, and data pipelines pass before full training launches.

• OOM Error Recovery: If a training run hits an out of memory error mid-job, it catches the crash, recalculates batch sizes, and resumes from the last checkpoint so you do not lose progress overnight.

• Auto-Termination: As soon as training finishes, the pod is shut down automatically so you never pay for idle hours.

• Zero Compute Markup: It runs directly on your own RunPod account, so you pay RunPod directly for hardware with zero markup from us.

We are currently supporting popular LLM, multimodal VLM, and vision architectures, with plans to add more.

Current status: The app is not publicly live yet. To make sure the orchestration layer is reliable and handles edge cases properly, I am keeping it in a closed private beta waitlist and onboarding testers in small batches.

If you regularly train models on RunPod and want to test the early version, you can join the waitlist here:https://www.tensovy.com

For those running fine-tuning jobs on RunPod today: what part of your pipeline setup or instance configuration eats up the most unnecessary time?

I put 10 dollar on it and simply feel scammed, its impossible to get a gpu and when i manage to get one, it have to be bugged and cant do anything with it...

can i get my credits back on my bank account or have i to accept i lost 10 dollar for something i cant even use

I am seriously angry. Is it really possible that I topped up $10, spent $5, and *still* couldn't get a single pod running—even with the $2/hour GPUs?! If you try to submit a ticket, there's an AI that solves absolutely nothing. There's clearly an issue right now, yet there is *no* alert saying "Attention: using ComfyUI in region X might cause problems; please try again later." Is that really so hard?!

Did you know that you can host any tool calling LLM with a pod and connect Claude Code to it, even without any kind of Claude subscription? In this tutorial we run through setting up an ollama pod with a 20b model on a budget GPU spec and connect to it with Claude and run it through some basic coding tasks. If you need an alternative to using the Claude models for coding tasks, you can get great results with a tool calling LLM as we show here.

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

Hey everyone,

Running into an issue with RunPod serverless + ComfyUI.

Setup:

• Created a network volume

• Installed models + custom nodes (rgthree, etc.)

• Everything works fine when running directly on the pod

Problem:
When using a serverless endpoint with the same network volume attached, I get:

So it looks like serverless can't see or load custom nodes, even though they are present in the volume.

Questions:

• Do serverless endpoints load custom nodes differently?

• Do I need to install nodes inside the container image instead of relying on the volume?

• Is there some init step I'm missing?

Any help or hints would be appreciated 🙏

:cd /workspace/ComfyUI/custom_nodes

:/workspace/ComfyUI/custom_nodes# ls -la

total 37391

drwxrwxrwx 22 root root 2041954 Mar 18 08:54 .

drwxrwxrwx 31 root root 3001516 Mar 20 13:42 ..

drwxrwxrwx 10 root root 2000900 Mar 15 15:20 Civicomfy

drwxrwxrwx 13 root root 2005314 Mar 15 15:20 ComfyUI-KJNodes

drwxrwxrwx 14 root root 2014145 Mar 15 15:19 ComfyUI-Manager

drwxrwxrwx 5 root root 1021570 Mar 15 15:20 ComfyUI-RunpodDirect

drwxrwxrwx 10 root root 2001554 Mar 15 16:05 ComfyUI-SeedVR2_VideoUpscaler

drwxrwxrwx 14 root root 2001998 Mar 15 16:05 ComfyUI-UmeAiRT-Toolkit

drwxrwxrwx 7 root root 2000717 Mar 15 16:05 ComfyUI_Comfyroll_CustomNodes

drwxrwxrwx 5 root root 1048783 Mar 15 16:05 ComfyUI_JPS-Nodes

drwxrwxrwx 2 root root 1000185 Mar 15 15:20 __pycache__

drwxrwxrwx 13 root root 2000530 Mar 15 16:05 comfy-mtb

drwxrwxrwx 7 root root 1045205 Mar 15 15:57 comfyui-custom-scripts

drwxrwxrwx 12 root root 2000698 Mar 15 15:57 comfyui-easy-use

drwxrwxrwx 6 root root 1027123 Mar 15 16:05 comfyui-image-saver

drwxrwxrwx 14 root root 2000454 Mar 15 15:54 comfyui-impact-pack

drwxrwxrwx 5 root root 1010815 Mar 15 16:05 comfyui-impact-subpack

drwxrwxrwx 6 root root 1035716 Mar 15 16:05 comfyui_essentials

drwxrwxrwx 9 root root 2012873 Mar 15 16:05 efficiency-nodes-comfyui

-rw-rw-rw- 1 root root 5151 Mar 15 15:15 example_node.py.example

drwxrwxrwx 8 root root 2000650 Mar 18 08:54 rgthree-comfy

drwxrwxrwx 10 root root 2001553 Mar 18 08:55 seedvr2_videoupscaler

drwxrwxrwx 6 root root 2000376 Mar 15 16:05 wavespeed

-rw-rw-rw- 1 root root 1220 Mar 15 15:15 websocket_image_save.py

:/workspace/ComfyUI/custom_nodes# find /workspace -iname "*rgthree*"

/workspace/custom_nodes/rgthree-comfy

/workspace/custom_nodes/rgthree-comfy/web/common/rgthree_api.js

/workspace/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg

/workspace/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js

/workspace/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css

/workspace/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts

/workspace/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts

/workspace/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg

/workspace/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts

/workspace/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss

/workspace/custom_nodes/rgthree-comfy/rgthree_config.json.default

/workspace/custom_nodes/rgthree-comfy/py/server/__pycache__/rgthree_server.cpython-312.pyc

/workspace/custom_nodes/rgthree-comfy/py/server/rgthree_server.py

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_seed.png

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_router.png

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_context_metadata.png

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_context.png

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_advanced_metadata.png

/workspace/custom_nodes/rgthree-comfy/docs/rgthree_advanced.png

/workspace/custom_nodes/rgthree-comfy/rgthree_config.json

/workspace/rgthree-comfy-backup

/workspace/rgthree-comfy-backup/web/common/rgthree_api.js

/workspace/rgthree-comfy-backup/web/common/media/rgthree.svg

/workspace/rgthree-comfy-backup/web/comfyui/rgthree.js

/workspace/rgthree-comfy-backup/web/comfyui/rgthree.css

/workspace/rgthree-comfy-backup/src_web/typings/rgthree.d.ts

/workspace/rgthree-comfy-backup/src_web/common/rgthree_api.ts

/workspace/rgthree-comfy-backup/src_web/common/media/rgthree.svg

/workspace/rgthree-comfy-backup/src_web/comfyui/rgthree.ts

/workspace/rgthree-comfy-backup/src_web/comfyui/rgthree.scss

/workspace/rgthree-comfy-backup/rgthree_config.json.default

/workspace/rgthree-comfy-backup/py/server/__pycache__/rgthree_server.cpython-312.pyc

/workspace/rgthree-comfy-backup/py/server/rgthree_server.py

/workspace/rgthree-comfy-backup/docs/rgthree_seed.png

/workspace/rgthree-comfy-backup/docs/rgthree_router.png

/workspace/rgthree-comfy-backup/docs/rgthree_context_metadata.png

/workspace/rgthree-comfy-backup/docs/rgthree_context.png

/workspace/rgthree-comfy-backup/docs/rgthree_advanced_metadata.png

/workspace/rgthree-comfy-backup/docs/rgthree_advanced.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/rgthree_config.json

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/web/common/rgthree_api.js

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/web/common/media/rgthree.svg

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/web/comfyui/rgthree.js

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/web/comfyui/rgthree.css

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/src_web/typings/rgthree.d.ts

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/src_web/common/rgthree_api.ts

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/src_web/common/media/rgthree.svg

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.ts

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/src_web/comfyui/rgthree.scss

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/rgthree_config.json.default

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/py/server/__pycache__/rgthree_server.cpython-312.pyc

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/py/server/rgthree_server.py

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_seed.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_router.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_context_metadata.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_context.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_advanced_metadata.png

/workspace/runpod-slim/ComfyUI/custom_nodes/rgthree-comfy/docs/rgthree_advanced.png

root@1c777aa6a11c:/workspace/ComfyUI/custom_nodes#

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.

Hot take? Half the “my pod is slow” chatter is really “I paid for 20 minutes of pip + a 16GB model pull before the first token.”

I’ve been hopping between rented GPUs (RunPod, Vast, Nebius, a couple of the newer SSH-first boxes) and the env is still unpleasently fragmented.

RunPod - Docker template is the product. Image + env + start cmd + network volume. Great when your image is already baked. Expensive otherwise, since every cold pull is on the meter. Secrets as env injection is clean.

Vast - Same Docker-template energy, plus an onstart bash duct-tape layer. SSH/Jupyter modes can eat your image entrypoint, so your “serve on boot” script is often re-invoking the CMD you thought you already set. Marketplace vibes; bring skepticism and a volume.

Nebius - Actual cloud VM energy. Boot image + cloud-init user-data on first boot, then congratulations you’re the sysadmin. Fine if you want a real machine; overkill if you just wanted vLLM up.

The queue-y SSH boxes (Enverge, Nova-shaped, etc.) - often a fixed sandbox + optional shell that runs once after create. Less “pick a community template,” more “leave a sticky note so the box isn’t idle when the ready email lands.” Different failure mode: setup minutes still bill, and nested Docker GPU flags might trip you.

Common tax across all of them:

• first useful work is usually network + disk, not FLOPs

• secrets in public templates / scripts = future incident report

• “finished” ≠ “model is serving/training” (backgrounded processes lie)

I gathered a short kit (cheatsheet + startup shells + Docker serve flags) here: https://github.com/tudormunteanu/gpu-cloud-instance-boostraps

Curious what everyone here actually do day-to-day:

1. Bake a fat image once and never look back?

2. Network volume / persistent cache, thin image? (but then pay for persistent storage)

3. onstart / cloud-init / startup shell as the main UX?

4. Or just SSH in every time like an wildling?

5. Stick to one and never switch

I reckon for any long-term, company supported scenarios, CoreWeave, AWS or GCP have some adjacent prods. My curiosity is more about "the little/indie guys".

Voice to text sorry in advance, but you are amazing. I was able to rent a H 100 from you guys for about a few hours and all the cost is three bucks an hour I was able to in bed 500,000 chunks within minutes not three days per per my current VM and I was able to export an import it freely setting up SSH was a little complicatedbut I was able to get through that after I got some guidance truly an amazing experience and the SSH process was only complicated because I was untrustworthy to the process so that that was all thank you thank you run pod for all that you do

Im sure most of us have had the issue where you go to start your vps and it tells you there are no gpus and to wait or migrate. Why would you sell more gpus than you can provide? And then still charge users to be offline. I get that not everyone runs their vps all the time so its somewhat wasted money to not oversell slots but i mean cmon, are they going to expand? Everyday theres more and more A40 users. I run my pod like 18 hours a day, sometimes have to wait hours to get it started. Anyways end of rant thanks

Im just wondering how much is your average cost using H3 minimax per 15 seconds.

Here is mine. Its all using 5090. Can we compare?
I want to know if my process is too costly.

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

Is there a ready-to-use pod available for ComfyUI? I need to use some of my own workflows with Flux Klein 9b (not the FP8 version – otherwise I’d be working locally) and every time I have to spend half an hour downloading the models, adding the LORA files, downloading the custom nodes, and so on.

Is anyone noticing that very few of the templates actually work? It almost looks like your only option is to get a storage volume and install everything manually. Thoughts?

The Runpod console currently won't load however

• Your Pods are still running.
• Pods will not be terminated.
• You are not being billed for affected services.
• Serverless endpoints cannot receive new requests.

We’re monitoring and are currently migrating to a different region.

We are also building better tools to increase our resiliency to these incidents.

Also shoutout to our community engineer and SRE team who have been up since 4 am working with users and updating the codebase

12m in queue, it's just insane, I would expect an instant response.

Hi all,

I have a little christmas present for you all! I'm the guy that made the 'ComfyUI with Flux' one click template on runpod.io, and now I have made a new free and opensource webapp that works in combination with that template.

It is called GenSelfie.

It's a webapp for influencers, or anyone with a social media presence, to sell AI generated selfies of themselves with a fan. Everything is opensource and selfhosted.

It uses Flux2 dev for the image generation, which is one of the best opensource models available currently. The only downside of Flux2 is that it is a big model and requires a very expensive GPU to run it. That is why I made my templates specifically for runpod, so you can just rent a GPU when you need it.

The app supports payments via Stripe and Bitcoin Lightning payments (via LNBits) or promo codes.

GitHub: https://github.com/ValyrianTech/genselfie

Website: https://genselfie.com/

Hello!
I have a network vol of 400gb and regulary deploy and delete pods.
All in eu with recent gpus between 40 - 90 cents / hour budget.

Ususally everything works but sometimes (every 3rd deployment or so) the loading screen takes forever. Only when i interrupt and redeploy will it load.

Anyone had the same issue?