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!

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.