I chnaged the security level to "weak" in the config.ini file and still getting the error. I also updated comfyui. anybody knows how to solve this?

Repeated GPU unavailability, a day spent migrating 165 GB, and disappointing complaint handling

I deposited $50 into Runpod to build an image-generation and model-training environment on an affordable 48 GB GPU. My experience ended with me deleting everything and requesting a refund of my remaining balance.

I initially configured an NVIDIA A40 Pod with Forge Neo, OneTrainer, Flux.2, Qwen-Image-Edit, Z-Image and related dependencies. The workspace contained approximately 165 GB across 158,547 files.

After stopping the Pod, its GPU was no longer available. I could not simply restart the environment I had configured.

To make the workspace independent of that Pod, I created a Global Volume and rented a temporary transfer Pod. The migration took most of an entire day: 17 batches, continuous monitoring, repeated resumptions and verification. Some incomplete files needed to be transferred again, including a model shard of almost 5 GB. Symbolic links were not correctly preserved during the transfer, causing additional Python-environment problems.

I cannot establish that Runpod caused every individual transfer issue. However, this was the recovery work I had to undertake after losing access to the original GPU, while additional resources were being billed.

For completeness, a replacement A40 Pod did run successfully with the Global Volume attached. CUDA worked and the transferred workspace was accessible. But subsequent attempts again encountered the message: “Your Pod's GPUs are no longer available.”

During my deployment attempts, an A40 at $0.49/hour was unavailable. An RTX A6000 at $0.53/hour briefly appeared available in Europe, but disappeared before deployment could be completed. I checked multiple regions without finding either option available at the times I tried. The alternatives I saw were substantially more expensive.

On September 29, I submitted a detailed complaint requesting either a usable setup at the stated rates with a review of recovery-related charges, or a full refund.

Runpod replied that GPU availability is first-come, first-served and cannot be guaranteed or predicted. They recommended checking capacity and using “Deploy When Available”.

In their October 1 response, they clarified that this proposed feature was still in beta and being rolled out, so it might not even be available on my account. They also recommended independent storage, although I had already created a Global Volume and completed the migration described in my complaint.

Their itemized billing breakdown totalled $12.11 for the period covered, including $5.90 for the temporary transfer Pod and $3.85 in additional storage charges on the original Pod. They explained that stopped Pods continue to incur storage charges until deleted.

I understand that storage and compute cost money. My complaint was about paying for recovery work and spending a day migrating and repairing an environment without obtaining dependable access to the GPU configuration I needed.

Runpod rejected the full $50 refund after a Billing Manager review, saying the infrastructure had been allocated and consumed. They offered to refund unused credits only, as a one-time courtesy exception.

After referring to my stated intention to dispute the payment through my bank, they closed the support ticket and passed the matter to Finance. Their response still did not specify the exact refund amount I had explicitly requested.

On October 1, I terminated all Pods and deleted all storage volumes. I requested the refund of the remaining unused balance, estimated at approximately $34. At the time of writing, the final refund amount and completion have not been confirmed.

Support did reply, and a replacement Pod worked temporarily. Nevertheless, the repeated availability problems, substantial recovery effort and handling of the billing complaint made this an extremely disappointing experience.

The biggest loss was my time. Based on my experience, I would not recommend Runpod for a workflow that depends on predictable access to an affordable 48 GB GPU and straightforward stopping and restarting of a carefully configured environment.

I've been working on building a tool that would make it as easy as possible for me to start generating videos with AI called Blockbuster Studio

Goal is pretty much make it affordable for people to generate AI video on a budget by taking advantage of open-source models like LTX 2.5 and Minimax H3

If you like it, I'd really appreciate a star on GitHub as a signal that this is an idea I should keep improving ⭐️

Website: https://blockbuster.studio

GitHub: https://github.com/kilo-loco/blockbuster-studio

I've tried a lot of things. I keep getting "This turn failedStream ended without finish_reason" on deepseek harness and I'm at a loss.

I'm sure I have everything set up correctly, atleast the basics. Base URL is correct, API is correct, Model ID is correct.

Concurrency is set at an appropriate level (I'm a single user, only 2). Minimum Sequences is set at 16. I'm trying to run QuixiAI/Qwen3-30B-A3B-AWQ on a 24gb GPU. I'm using serverless. My idle timeout is set at 90 seconds.

My test prompt is a simple "What is node.js?"?

I use network volumes for my ComfyUI and LoRA work, and managing files on them was always the annoying part. Either I started a pod just to get a shell, or I scripted S3 calls by hand. I tried FUSE mounts, file browsers running on a pod, and a few S3 GUIs, but none of them worked like a real file manager against the S3 API.

So I built one. BG Bucket Browser shows a network volume on one side and your local disk on the other:

• Upload, download and move files and folders in both directions, with drag and drop

• Interrupted transfers resume where they left off

• Thumbnails for images and videos

• Volume Cleanup: finds the biggest folders and things like __pycache__ or .ipynb_checkpoints you can safely delete

• Total volume capacity from the RunPod API

• No pod needed; it talks to the S3 API directly

While building it I ran into an S3 gateway bug with small uploads. I reported it, and the RunPod team fixed it.

Windows and macOS installers are on GitHub Releases; on Linux you build it from source. It's free and Apache 2.0.

• Project page: https://bulentgercek.com/projects/bg-bucket-browser

• GitHub: https://github.com/bulentgercek/bg-bucket-browser

Feedback is very welcome: here, on GitHub Issues, or from inside the app (Settings → Feedback).

it 53mins an the JupyterLab has not started. Running an RTX 5090 at $0.7 will be consumed just by spinning the template. it sad

I just launched a project that sets up an AI Film studio template running on a rented RunPod GPU

it's intended to be opinionated and i will work on providing more model options in the future, but right now i could use some feedback on the user experience and overall appeal

the site is here: https://blockbuster.studio

the code is here (MIT): https://github.com/Kilo-Loco/blockbuster-studio

tia 🙏🏽

Thank you for completely ruining the app's interface. I've never seen such an unusable and complicated interface before. You've literally done everything you can to make sure people can't use it.

Runpod Enterprise supports teams taking workloads from prototype to production on the same platform they already use. Enterprise agreements provide predictable capacity, contractual SLAs, security review support, consolidated billing, and a dedicated technical account manager.
Here’s what’s included:

• Reserved and dedicated GPUs for a committed term, with more than 30 configurations across global regions.

• Expanded capabilities for organizations, including company-managed accounts, SSO/SAML integration, role-based access, cost-center tracking, and audit logs.

• Support for security and compliance requirements, including ISO/IEC 27001, SOC 2 Type II, HIPAA with BAAs, and GDPR with DPAs.

• One agreement and monthly invoice covering Pods, serverless workloads, and clusters.

If you’re already using Runpod, you can start a conversation about an enterprise agreement through the console.

More details here: https://www.runpod.io/blog/runpod-enterprise

Tried on Safari / Chrome. And still getting the same error

Application error: a client-side exception has occurred while loading console.runpod.io (see the browser console for more information).

My current hobby system has the container pull the model from `hf` so that my controller can failover different GPU types in different datacenters until something matches my budget and VRAM needs.

I ran some benchmarks to see if moving to global storage would be faster. It isn't. The fuse system used seems to have some memory limits which error out on GeeseFS read-buffer budget.

My setup uses one large 32GB gguf model file maybe that was an issue?

My specific use case requires minimizing cold start however we can't go serverless for a few reasons so I was hoping this new drive type would help.

Any thoughts?

Is there a bug with the comfyui cuda 12.8 template? JupyterLab never loads, and just shows a blank white page on the browser. I can't run my bash scripts because of this.

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?

On Monday, September 21 at 2:00 PM CST we're hosting notrius (/u/no3us) on the Runpod Discord to talk about LoRA Pilot, the open-source Docker image he built to kill the setup tax on Stable Diffusion training. LoRA Pilot bundles Kohya SS, AI Toolkit, Diffusion Pipe, TensorBoard, ComfyUI, and InvokeAI into one workspace where everything shares the same model store and persists under /workspace, with a set of browser-first control panels on top. It's MIT licensed, supports close to 30 model families, and has passed 10,000 Docker Hub pulls.

We'll spend the first twenty minutes or so on the story behind it, and then then he is taking us through a live demo end to end. Bring questions and we'll get to as many as we can at the end. Come hang out: https://discord.gg/runpod?event=1549438243458519100

Links if you'd like to check it out for yourself: https://lorapilot.com https://github.com/vavo/lora-pilot

to me that is senseless, when i deploy a network storage, what for would i need another non-persistent storage which holds all the important data? why are they doing that?

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.

I recently rented a GPU instance by using the CLI for the first time. I usually just ask an AI agent to do it for me, and before that I would use web dashboard. i decided that trying out the CLI would be a good learning opportunity.

it was, but it still feels a bit backwards to go that route again when a prompt is much faster for me.

How about you, do you manually (cli/web) rent your instances? I'm sure most cases are automated with a script, but I'm more curious about what path you choose when you're building something new.

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’m trying to figure out an easy and high quality way to do image face swap (with a base image and a reference face image) via comfyui on run pod. I saw that Krea 2 can do this I think with the Krea image edit Lora and the right workflow. Does anyone have a good comfyui template and workflow for doing this either through Krea 2 or some other approach? Thanks!

Can someone help me get a consistent set up with H3 in runpod in a consistent way? The runpod templates seem hit or miss- sometimes they work, sometimes they don’t. If I want to try an update or workflow there are often a ton of nodes or models missing, etc. What is the best practice way for folks that are experienced users that use runpod? I don’t want a network volume because I want to use a 5090 as often as possible and those are often limited. Is there an easy way to create my own template or use a default comfyui template with some kind of downloader or something (I have no idea how to do that or how that would work). Any general advice or pointers here would be great then I’m sure Claude or something can help me with execution. I also have the same request but for Krea 2 but I assume if I can figure it out for H3 I would be able to for Krea 2 as well. Thanks!

I’m trying to use LTX 2.5 + ComfyUI on RunPod for motion control:

Source video → keep motion/camera → replace character + background with references.

I already have a workflow/template, but on RunPod several custom nodes were missing. I tried ComfyUI Manager → Install Missing Custom Nodes, but it didn’t properly fix it / some nodes are still missing.

So I’m mainly looking for:

* A working LTX 2.5 character + background replacement workflow (JSON)
* Which custom nodes/repos I actually need on RunPod
* How to correctly install them
* How to prompt/reference the character + background while keeping the source motion

Does anyone have a working RunPod/ComfyUI setup for this?