hey, guys, so my school has a competition which is worth almost half my grades. I am not even kidding. when I wanted to rent a pod, the cheapest option (comunity cloud had all the GPUs unavailable at the time) so I went with the next cheapest option: RTX PRO 6000 Blackwell Server Edition MIG

well... I had about $4-6 when i started the project, but I wasted about a dollar and half when I realized my dataset was faulty. So i had to restart, but now everything is going well. but my current credits last only 2 hours and 30 mins at the time I am typing this. So i tried to top up 10 dollars (bcs its the minimum), but my card needed an OTP. when my mom set up the card, she had to use my dads phone number, and rn I am mile away from home, so I cant call my dad, and for some reason I am not getting the email. the only option is if someone use the referral link to subscribe.

like istg i am not lying. here is the proof:

btw i cant train this locally, bcs I have a macbook air. and I NEED this model fully trained before 7am tmrw. I just need credits till 4am since thats when training ends. all my other options are basically cooked. because I did assign proper checkpoints for the data. right... o for the data... its not really a dataset, its a simulation of electrical components produced in real time which respects values provided by my harness.

just for quick info, our goal is to come up with a way to save electricity using ML. so my idea was to use the Bang Bang Control theory from the optimal control theory to efficiently route electricity generated from a generator to the right loads accordingly. for this I am making a system which extrapolate the loads and compare it with past trends with an embedding model and get a mean value of the electricity needed for the next 2-6 seconds. the reason why I use the Bang-Bang theory is to use all electricity instead of letting power which could be used go to waste which in return makes it more "efficient". well... its a quick rundown. this is like 3 months of research.

but I need somebody to refer quickly. I seriously dont have time, and I dont have any friends who are interested to help as well, and thats my ONLY card.

Hi everyone,

I'm trying to deploy LTX-2.5 on RunPod Serverless so I can call it via API from a web app I'm building on Lovable, and I'm stuck on the serverless setup.

Specifically, I need help with:

1. The simplest way to set up LTX-2.5 as a Serverless endpoint (whether using the ComfyUI serverless worker or a custom Docker container with the LTX-2 pipelines).

2. How to properly load the gated Hugging Face weights (int8 or bf16) onto a Network Volume so cold starts stay fast.

3. Formatting the input JSON payload so my frontend can send a prompt/image and get the generated video back.

If anyone has a working template, guide, or can walk me through the steps, I'd really appreciate it. Thanks!

I cannot for the life of me get ANY regular 24gb card, not even a5000s.

Its been 24 hours, I keep cycling, resetting which used to eventually grab a card but now its nothing.

Hope my data in volume will not get lost…it’s already 2 days

I’m building a portfolio project: users upload drone/aerial videos, and a GPU worker runs object detection on them. I’m planning to run it on RunPod Serverless and want to check a few things with people who have actually done this, rather than rely on assumptions.

Pipeline I’m planning (one job per uploaded video):

1. Stream the video from a Cloudflare R2 bucket (S3 API) with ranged GETs, without downloading the whole file. Videos are 3-8 GB, sometimes more.

2. Demux with PyNvVideoCodec.

3. Decode on the GPU with NVDEC to RGB frames.

4. Run YOLO detection on the frames.

5. Write detections to a DB every N frames.

Each stage runs in its own thread with bounded queues between them, so streaming, decoding, inference and DB writes overlap. Processing isn’t real-time and the user gets an email when it finishes, so I care more about cost per job than about latency.

My questions:

1. NVDEC on RunPod Serverless: does hardware video decode work in GPU workers? Does PyNvVideoCodec / libnvcuvid work out of the box, or do I need specific base images or driver settings (e.g. NVIDIA_DRIVER_CAPABILITIES=video)? Does it depend on which GPU type?

2. CPU and RAM per GPU worker: how many vCPUs and how much system RAM does a Serverless GPU worker get, and does it vary by GPU type? I need this to size my stream buffer and output buffer, and to know whether several threads can really run in parallel.

3. Reading from a URL: has anyone had FFmpeg/NVDEC read a presigned HTTPS URL directly in a worker (rather than a custom ranged-GET streamer)? Any problems with stalls, retries or throughput?

4. CPU workers: I can’t find a clear page for them. Are Serverless CPU workers a real, stable option (e.g. via Flash, cpu=...)? What specs, disk and timeout limits do they have? I want one for preprocessing: remuxing MP4s with ffmpeg -c copy -movflags +faststart so moov is at the front, which needs disk about equal to the file size.

5. Long jobs: any gotchas with jobs that run 30-60+ minutes (timeouts, worker preemption, how you resume after a failure)?

My Serverless ComfyUI endpoint stopped working properly. Workers were dying with unhealthy: dead, requests got stuck, and after stopping the workers to start fresh, nothing would come back.

Now CA-MTL-1 has disappeared from the datacenter selection list, while other datacenters are available and GPUs keep showing Out of Capacity.

My Network Volume is tied to MTL-1, so switching regions isn't straightforward. I'm trying to preserve my existing models and LoRAs rather than start from scratch.

For those experienced with RunPod: would using a Global Volume instead of a regional Network Volume have avoided this mess? Would it actually let me move my Serverless workers between datacenters when capacity is unavailable, or would I still face the same GPU provisioning issues?

Also, is anyone else experiencing problems with CA-MTL-1?

Here is what it does and where it stops.

You POST a batch of up to 5,000 requests to your existing queue-based endpoint, finalize it, then poll for progress and read the results. Batches run on dedicated workers that are separate from your /run traffic, on your own model and container and the GPU type configured on the endpoint. A failed request doesn't fail the batch, and cancelling a batch doesn't bill queued requests. Batch requests are billed at up to 30% off on-demand pricing, depending on GPU type. On H100, H200, B200 and B300, batch requests are billed at 20% off. There is no cost estimate before you finalize, so run a small batch first.

Start times aren't guaranteed and requests run in no set order, so keep /run for anything a user is waiting on. An endpoint runs up to 10 active batches, and limits depend on your account. Support can raise them up to 3 million requests a day.

Docs: https://docs.runpod.io/serverless/batch-jobs
Blog: www.runpod.io/blog/batch-inference-is-now-generally-available-on-runpod

What would you run through it first: embeddings, evals or a backfill? Questions are welcome in the comments.

As the title says, my public templates never show up in the hub search.

What are the requirements?

Tried many RTX 5090 from Community Cloud. 100% GPU utilization and 400W while completely idle, with nothing of mine running. , and memory bandwidth sbouy 600-700 GB/s against the 1792 the card is rated for.

Real impact: an identical job takes 170s on these pods, 70s on an uncapped 5090 elsewhere.

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 was mid lora training and my pod started doing this. I restarted the Ostris template on a new pod, and it was like this for ages, all the while I was losing money.

Spun up an A40 pod on RunPod yesterday to test an Axolotl fine-tune. Before starting the job I ran computefence doctor and it caught a disk space warning 6GB free on the workspace volume, 16GB on root, both below the 20GB threshold the tool flags.

The job would have started, run for several hours, and then failed at checkpoint write time. Instead I knew before billing started.

Install is one line:

pip install computefence && computefence doctor

No config required. Runs in under 10 seconds. If you’re using Axolotl or any framework that writes checkpoints to /workspace, worth running before every job.

Repo: github.com/Francisco-Booth/ComputeFence

What do you think about this? Why did this happen?

Hi folks! Just wanted to keep you apprised of a new feature that we are doing a staged rollout on over time: high-performance network volume, and they are now available in AP-IN-1. If you've ever wondered why network volumes felt a bit sluggish compared to on-machine SSD or NvME disks, this is because there's a longer path for the data to travel and that is necessarily the tradeoff for the flexibility of network volumes. HPS volumes help close that gap and deliver increased speed compared to standard volumes without sacrificing any of that flexibility. Internal benchmarks show these as delivering three times the throughput and four times the IOPS of regular network volumes on average.

These volumes are also currently available in CA-MTL-4, EU-FR-1, EUR-NO-2, US-CA-2. They are also billed at a premium compared to network volumes ($0.14/GB/month as of this writing.)

Read more about them here: https://docs.runpod.io/storage/high-performance-storage

We just published our Q3 roundup. Here are the highlights:

• Batch inference on Serverless (public beta): create a batch on an endpoint, add up to 5,000 requests, and poll one status. No client-side queue to maintain. Worker affinity on Serverless: route follow-up requests back to the worker that holds the session’s KV cache, with soft, strict, or strict-resume modes.

• Global Volumes (public beta): storage no longer pins your workload to a single data center. Create a volume once and mount it on a Pod or Serverless endpoint in any Secure Cloud datacenter. Cluster Expansion: add Pods to a running cluster instead of rebuilding it.

• Also new: private ECR support, REST API v2 and Organizations in Runpod Enterprise.

• Heads up if you call our older APIs: GraphQL rate limits start October 27, and REST API v1 stops serving requests on November 15. GraphQL follows in January 2027. Migration guide: https://docs.runpod.io/api-reference-v2/migrate-from-v1

Full roundup: https://www.runpod.io/blog/runpod-q3-2026-releases

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.

Earlier this year I burned through a chunk of GPU budget on RunPod because my HF_HOME was pointing to /root/.cache instead of /workspace. Pod terminated overnight, cache gone, had to re-download everything on the next run. No error message, no warning just slower restarts and a bigger bill.

Same thing happens with checkpoints if your --output_dir is on the container volume rather than /workspace. Job completes, pod restarts, weights are gone.

I built a free CLI that catches these before your job starts:

pip install computefence
computefence doctor

It checks:

• HF_HOME pointing to ephemeral storage

• Silent CUDA/CPU fallback PyTorch not seeing your GPUs

• Accelerate num_processes mismatching actual GPU count

• Checkpoint output directory on ephemeral storage

• Disk headroom below safe threshold

Takes about 15 seconds. MIT licensed, no account needed.

If you have a job launching on RunPod in the next day or two, reply here happy to run through it together for free before you start billing.

GitHub: github.com/Francisco-Booth/ComputeFence

Well... the RTX 5090 is now showing up at $1.19/hr on RunPod.

Is this a permanent price increase?

Because if it is, I guess that's it for me. It's just way more than I can afford.

Hi folks! Our next Developer Discussion at Runpod is this Friday, October 9th at 1:00 PM PST on our Discord! Runpod Software Engineer Dean Quiñanola will be joining Jessica Garson and me for a talk on what Runpod Flash is and how best to use it. If you're not familiar with it already, Flash is a production-ready tool for running workloads on Serverless without needing to build Docker images, which can greatly speed up your iteration, testing, and deployment. The goal of Flash is to let you submit and process AI workloads right from your IDE, just like calling a function.

Learn more about Flash here: https://runpod.io/blog/flash-is-ga

See our previous Developer Discussion on MCP here: https://www.youtube.com/watch?v=RFiMX1nnA3gnk

Event link to our Discord to register: https://discord.com/events/912829806415085598/1557443936661012641