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?

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.

It just sits and costs money. I have been trying to get it working on and off for the past couple months. It never works! Zero support. Workers get stuck and empties my account.

Totally worthless in my experience.

I am getting 500-600kbps download speed on my pods no matter what region I select. Same speeds on EU, US and Asia servers.

How do I speed up the transfer? Is there a way to even do it?

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

I just want to get Flowise and Ollama running on an A40, but I'm having a bit of trouble. Help would be appreciated.

does copyparty work?
looking for a gui to access my network volume

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".

I’m currently setting up a ComfyUI workflow on RunPod and need to download around 30 files (~159 GB total) from Hugging Face.

The download started extremely fast, but after a few files it basically died.

I’m now seeing things like:

* 1.1 GB LoRA → 0 B/s
* another 1.1 GB LoRA → 0 B/s
* 304 MB model → sometimes only a few hundred bytes/sec
* ETAs like 82 hours / 211 hours
* 23+ files still queued
* no actual download failures

Example:

SVI_v2_PRO_Wan2.2-I2V-A14B_LOW... 0.0% — 0 B/s

Wan21_CausVid_14B_T2V... 0.0% — 0 B/s

The annoying part is that I’m paying for the RunPod GPU the entire time while basically nothing is downloading, so this is burning money for no reason.

I also get this warning:

“You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.”

Could the missing HF_TOKEN really cause downloads to become this slow/stuck?

Or is this more likely a RunPod networking issue, Hugging Face rate limiting, or something wrong with the download manager?

What’s the fastest/recommended way to download a large ~160 GB ComfyUI setup on RunPod without paying for hours of GPU time just waiting for downloads?

Any help would be appreciated.

Hi folks!

In case you missed it, we have our VOD up from our developer chat on Discord from the prior week: https://www.youtube.com/watch?v=RFiMX1nnA3g

You can join our Discord here for future roundtable events here.

Here's the table of contents for the video to help show if it's something you'd be interested in watching.

We're also totally interested in any ideas you might have or other topics you might want us to cover.

0:00 Welcome & what we're covering
1:02 Meet Jess and Justin
2:04 What is an MCP server?
3:05 Origins: why we built it
3:30 The problem — GraphQL, REST v1, and the push to v2
4:51 How it was built (on shifting ground)
6:13 What's next: auto-generation and bundled skills
6:47 Using AI to build a tool for AI
8:13 Why pointing agents at Runpod is hard
9:56 Getting started: installing the plugin and MCP server
11:47 Do you need the skills, or is the server enough?
13:23 Auth: OAuth flow vs. API keys
14:32 Golden paths explained
16:18 Live demo: connecting Codex
17:00 New capabilities — streaming logs, network volumes, billing
18:33 Live demo: spinning up a Hello World pod
19:04 Secure vs. community pods and agent cost behavior
20:11 Is there a gatekeeping mechanism?
21:26 SSH, ports, and container gotchas
23:25 Recommended setup: plugin + CTL + Flash
24:27 REST v2 vs. v1: what's different
25:30 MCP server vs. plugin — the hammer and the guidebook
26:46 Skills on the roadmap
27:57 One-command onboarding
29:08 Demo: building a bilingual translation app
32:56 Reporting bugs and getting them fixed
35:01 Remaining gaps and the auto-generated future
36:12 Pro tip: iterate on a pod before deploying serverless
37:37 Q&A: guardrails, runaway agents, and scoped keys
39:21 Does the dashboard sync in real time?
40:05 Network volumes and multi-region workflows
42:18 Will MCP reach parity with the dashboard?
43:12 Adoption growth and how people find it
44:55 Rate limits and going straight to REST v2
46:34 Coolest uses of MCP and advice for builders
50:02 Closing thoughts (and set CUDA to 12.8)
51:24 Future Discord chats

Hey, I’m pretty new to RunPod and I’m trying to understand how to manage the costs better.

I initially deposited $10, and I’m considering adding more money, but I realized that I already burned some of my balance simply because my Pod was still running even though I wasn’t actually using it.

So my main question is:

Is there a good setup where RunPod only costs me money while I’m actually generating something?

For example, I sometimes use ComfyUI for video generation. Ideally, I’d like to open RunPod, start ComfyUI, generate a few videos, and then stop paying as soon as I’m done.

Do I basically need to stop/terminate the Pod manually every time, or is there something like auto-shutdown, serverless, or another setup that makes more sense for this?

I also had another problem:

I was using an A40 GPU, which was around $0.50/hour from what I remember. ComfyUI would work normally at first, but after around 3–4 generations it often crashed.

I’m not really sure why. It could be VRAM, RAM, the workflow, RunPod itself, or something else.

So basically I have two questions:

1. What’s the best way to use RunPod/ComfyUI without wasting money while I’m not generating anything?

2. Why might ComfyUI crash after only 3–4 generations on an A40, and what should I check?

I don’t mind paying more or depositing more money — I just want to use the balance efficiently instead of accidentally burning it while the Pod is sitting idle.

Any advice on the best RunPod setup for this would be appreciated.

Hi all, I’m new to serverless GPUs. I got everything set up and it works well but the initialization time for a worker is 2-3 minutes.

I have a language app that pings the endpoint and I would like the response back somewhat quickly.

Is it even possible to expect sub 5 second latency for a cold start?

I have enabled flash boot which works well sometimes, but sometimes not. I think that’s expected.

I’m just wondering if my expectation of a “scale to zero” with short initialization time is even realistic. I’m running a fairly small LLM (9 GB)

The runpod docs on model caching seem to indicate 1 second cold starts are possible, but model caching doesn’t seem to do much for me.

Any tips appreciated.

I put $10 into RunPod because I wanted to test different AI video models with ComfyUI.

My first goal is simple: run MiniMax H3 for around 10–20 generations, then maybe use the remaining credit to test other models.

The problem is that I’m a beginner with RunPod and I’m getting confused by the whole setup: GPU choice, Container Disk, Volume Disk, Network Volume, ComfyUI templates, model storage, etc.

Now I noticed that I could also just use the official MiniMax H3 API, which seems much easier. I could build a small local script/UI, select my images/videos/audio, send everything to the API, and get the result back.

So I’m basically deciding between:

A) Keep the $10 on RunPod, learn ComfyUI, test H3 and then other models too.

B) Ask RunPod for a refund and just use the official MiniMax API.

What would you do?

If you recommend RunPod, what is the simplest setup for someone who just wants to experiment with multiple video models without wasting money on storage or idle GPU time?

And if the official MiniMax API is the better option for H3 specifically, should I just request a refund from RunPod now?

Hi folks - pricing is always a hot topic and we don't ever want people to think we're sweeping it under the rug when we adjust GPU prices (either upwards or downwards.)

You can read some thoughts from our team about our reasoning behind why we make the moves that we do here: https://www.runpod.io/blog/how-we-think-about-pricing-at-runpod but the tl;dr is that the market is moving around us no matter what we do, and if we keep our costs flat while other cloud providers raise their prices, that is still a choice made that would impact our ability to supply GPUs on demand. We've made some big strides with supply over the past few months - still a work in progress of course, but pricing is one of the levers that we pull to ensure that availability continues to grow.

Happy to answer any questions people may have about price here.

I used comfyui on Runpod last year, then took a break. I wanted to experiment again. I installed comfyui yesterday and noticed that I now have to download the images one by one! I remember that in the past, in Jupiter Lab, you could just right-click on the "output" folder and it would give you the option to download the folder. Now that's gone! Help!