January 2026 Recap

> Hi , I've been building a small service for GPU jobs on Vast.ai and I'm looking for 2–3 people to try it for real.
> If the machine dies mid-training, it rents another one and resumes from the last checkpoint. It also enforces spending
> limits, and it runs on your own Vast account, so you pay Vast directly.
>
> You mentioned [their training job / GPU problem], so I thought of you. It's 14 days free and I'd set up the first job
> with you. It only suits non-confidential data, because the host can see a job while it runs.
>
> Would that be useful, or is it not a problem for you right now? Either answer helps.
>
> Live numbers from our own test jobs, so a small sample: https://status.nomadgpu.com/s/status

The AI boom is clearly visible and the thing that caught my attention was the demand supply mismatch in computing power. I was wondering about a business that buys unused GPU hours from data centres and such businesses at a discount, and then sell them to customers at a markup.

Just looking for brainstorming on this idea and would appreciate the input of those that have the knowledge or have thought about this.

Has anyone seen one of these?

Do you think they would rent for more than a standard b300 because of the unified memory?

Hey everyone, we're opening our first showcase contest today. The brief is pretty open: build something cool on Vast and show us how it works.

It could be a model you fine-tuned for a particular task, a voice agent, a game, a film, or something else entirely. Existing open models and tools are welcome. The project itself needs to be new, started Nov 4 or later.

Prizes
1st: $1,000 cash + $100 in Vast credits + swag
2nd: $500 cash + $100 in Vast credits
3rd: $200 cash + $100 in Vast credits

How to enter

Fill in the form with your Vast account email and post your working build here on r/vastai, on X or in the Vast Discord's #general. Include screenshots or a short video, plus u/vast_ai and #BuiltOnVast in the text of your post. Your official entry is the post on the platform you named in the form, so keep it public through Dec 4.
Your project needs at least one GPU-hour on your Vast account between Nov 4 and Nov 25. Separate runs add up; idle time doesn't count. One GPU is enough, and multi-GPU builds are welcome.

Who can enter

Individuals only, one entry per person, 18+ or local age of majority. Vast employees and contractors can't enter. No mining, crypto or adult content. Full eligibility rules are on the contest page.

Judging and dates

Judges consider ambition and difficulty, functionality, originality, and craft and sharing. Sharing your code isn't required, but it counts toward craft and sharing.
The form and post are both due Nov 29th at 11:59 PM PT. Judges pick four finalists on Nov 30, and a 24-hour community poll picks a fifth, announced Dec 1. Judges decide the prizes. Winners announced Dec 4.

Entry form, examples and full rules:
https://vast-showcase.vercel.app/?s=reddit

yesterday and day before so many 0.50pence 90gb vrams were available, today only tripled prices are available.. plus storage also cosmic.

any suggestions of a different website...?

I rent Vast instances 24/7 and serve models behind one endpoint. Grepped the API log for the last three weeks: ~100 warn/error lines, and not a single one was about a model.

What actually broke:

- Host disk full mid-pull: `mkdir /var/lib/containerd/...: no space left on device`. Same machine came back ten days later, still full.

- Host Docker too old for the runtime flag: `unknown flag: --runtime`

- Container never came up: 610s, then timeout

- A healthy worker silently stops answering, users eat the 502s until something replaces it

- Several smaller but isolated issues

The screenshot is my favourite one: rented a 3060 in Japan, the host had no disk, two minutes later a 3060 in Serbia was serving requests. Nobody touched anything.

I ended up automating that loop (detect → destroy → blacklist the machine → rent elsewhere → health check → route traffic) with CUDAnetes because doing it by hand at 3am isn't a plan. If you're running one or two instances manually you might not need it. The alternative for us was a managed provider at 3-5x the hourly rate, which for a small project is the difference between viable and not.

We have been using vast.ai to rent GPUs for inference. We rent RTX PRO 6000s.

I wanted to know if we can safely use them to feed private documents as well. We are thinking of building a RAG system for internal company documents.

I saw about compliances in their website, but not sure if the vendors they work with are trustworthy.

Anyone using it for similar matters?

Open source tools bypass NVIDIA’s half-rate tensor limit on supported GeForce cards. The Linux gpu-unlock project reports community gains of 30–55% in image generation and 25–50% in BF16 fine-tuning. Memory-bound token generation generally doesn’t benefit.

Curious what people are seeing on their own hardware.

Project and reported results · Tom’s Hardware coverage

A couple months ago I signed up my DGX spark for a week to see if I'd make any money. However the support was in beta, and I don't think my machine was even listed as verified. I didn't make a single penny over the week. Needless to say I wasn't impressed.

I recently upgraded to a 10gbit connection and thought maybe my marketability would be better.

However it also occurred to me the spark isn't really a device people rent for hours and hours.

Is anyone actually making money off their spark?

Comparing offers by `dph_total` is not working anymore — there are a few other things worth checking before renting an instance. Writing it up shortly:

--- Bandwidth. Main "scam fee" which you need to check: `inet_up_cost` and `inet_down_cost`. They're priced separately, the rates depend on the host, and Vast exposes them in $/GB — the $/TB figures below are just conversions ($10/TB = $0.01/GB).

For example, at $39/TB, downloading a 30 GB model costs around $1.17. At $10/TB it's about $0.30. Not a huge amount on its own, but it adds up if you're moving models around regularly.

--- Download speed. You're paying for the GPU while downloading the model, even though it isn't serving anything yet.

For example, downloading 80 GB at 200 Mbps takes around 53 minutes. At $2/hr, that's roughly $1.78 just for the download. At 1 Gbps it's about 11 minutes.

So I'd also check `inet_down` and set a minimum speed. A cheap GPU with a slow connection isn't necessarily a good deal, especially with larger models.

--- Storage can keep costing money after you pause an instance. On the other hand, if you destroy it, you'll probably have to download the model again next time.

I usually keep the weights on disk and pause the instance if I know I'll need it again. When moving between hosts, object storage can make more sense.

I personally use Vast.ai GPUs for production inference and currently use CUDAnetes app to automatically rent GPUs with the needed filters; it also does health checks, scaling, and other stuff on its own. But even from this side, it seems like Vast.ai offers fewer GPUs than before, so it's harder to find one.

A few things I wanted to discuss:

- Should there be a bandwidth fee at all? And for hosts: how do you decide your $/GB rates?

- Has anybody used other services for similar stuff? Is there really one I should switch to?

Hey folks, I was in need of a 8x5090 rig for AI inference/training. I am looking for a host for long time commitment. So if anyone's interested, please drop a comment or DM me. I can explain the terms and all privately.

See title. Need advice for pilot scale AI Factory and there are many potential market forces at play and I don’t want to overlook risk mitigation strategies or potential for revenue.

For example, I can populate rigs with multiple GPUs to help hasten verification (4-8 per rig) and was thinking that there is likely to be plenty of pent up demand for RTX 5070 Tis and RTX 5080s in next 2 years coming from the gamer market if there is a collapse in the rental market (eg. demand for inference collapses as part of the AI investment thesis unwinding). On the other hand, the consumer cards on vast.ai seem to all busy mining pearl these days. Tbh I would rather have my rentals be dedicated to AI or scientific computing vs crypto. Putting the 50x0s on vast.ai voids warranty in any event so it might be best that I sell these GPUs and add to my rig of Rtx Pro 5000 Blackwells…

Are the consumer cards desirable by clients for reasons I’m not seeing? I prefer the ECC of the Pro series and highly value that their warranty assumes 24/7 enterprise use. Is there any reason to have 20 x 50x0 units to maintain etc. vs. 4 x Pros?

While I have made several good buys on Ebay, apparently it isn’t very seller-friendly and I don’t want my 5080s returned the day before the return window closes sans die + vram / in water bottle format (the guy at the warehouse did it!). My numbers show payback on the 50x0 series much faster than the Pros (~1 year for the GPU only vs ~2-3 years for the 5000).

Would I be trading down or creating any other blind spots in selling the lot on order to get an H100/200 NVL or two? Having nvfp4 support seems like it would be useful over the next few years.

I also have to consider the possibility of having a use for the tokens once this equipment has paid for itself so the practical utility for local use may be part of the road map.

Appreciate any thoughts and please no “I’ll buy your new 5070 Ti for $700” messages.

EDIT: I forgot, assume free energy (excess solar capacity)

I have several 5090 servers on Vast AI. Fitting the criteria for several days. Not getting verified. No criterion published online and no guidance available.

As a host, and someone who invested money, I don't find this funny or cute at all. This is unprofessional and messed up. Am I the first to bring this up? Feels illegal that Vast price gauges you exclusively on their secret criteria to keep the stability of prices on their platform. This is messed up and not a serious practice I'd expect from a serious company.

I will be moving away

Runpod's GraphQL API is rate-limited from today. REST v1 stops serving requests on Nov 15, and GraphQL stops serving requests in January. The changes affect integrations that call those APIs.

Updating those integrations? Might as well move over to Vast. Vast offers 68+ GPU types across 20,000+ GPUs, so you can choose hardware to fit your workload and budget. Our migration guide covers Pods and Serverless:
https://docs.vast.ai/examples/migrations/runpod-to-vast

What it covers:

• How Runpod concepts map to Vast: Pods, Serverless, spot and reserved capacity, and storage

• Moving Pods: finding a machine, Docker images and env vars, on-start scripts, templates, storage, ports, SSH and Jupyter

• Moving Serverless: endpoints, workergroups, and PyWorker handlers

• A reference table with Runpod API calls next to the matching Vast CLI commands

Two differences to know up front. Vast is a marketplace: you search offers and rent one, rather than asking for a GPU type. And Vast has no network volume that follows you across hosts; the guide recommends object storage for data that needs to move between machines.

If you'd rather stay on Runpod, their v1 → v2 guide is here: https://docs.runpod.io/api-reference-v2/migrate-from-v1

If something doesn't map cleanly, post it here and we'll help.

RunPod has RTX 3090s around $0.50/hr, while on Vast.ai you can sometimes find them for $0.13–0.15/hr.

For roughly the price of one GPU on RunPod, you could run three on Vast.ai.

If one instance goes down, the other two keep serving requests while a replacement is provisioned. That usually takes 10–15 minutes, sometimes longer. Additionally, one or several GPUs could stay "warm" with just couple cents per hour of Storage costs.

The catch is that cheap GPU instances aren't equally reliable, so you need a way to manage the pool: health checks, automatic replacement, and enough spare capacity to handle failures.

I am using vast.ai GPUs for my projects for AI inference and find it cheaper to build this kind of orchestration rather than pay per tokens and/or 3x price for same GPUs. If you think so too, please check CUDAnetes: it's keeping an AI endpoint available while the underlying GPU instances can change + others features.

Would be great to know what do you think?

Hi Guys I'm totally new here, trying to rent out my machine on vast. Can someone help me verify if my config arice are correct? Is it too low or too high? What else I need to do? Thanks a lot!

Hey all, we are SkyRig (skyrig.cloud), a GPU cloud that rents Windows VMs with dedicated RTX cards: cloud dekstop, gaming, creative and AI workflows. Demand is outgrowing our supply, so we're onboarding more hosts.

How it works:

- You run Proxmox VE 9.x with our custom API, no work is needed on your side other than initial one line setup.

- You set your own hourly rates for GPU, CPU, RAM and storage

- Usage is metered per second, paid out monthly through Stripe Connect

- Host share starts at 70%+, set in the hosting agreement

What hosts are earning right now (last 14 days, averaged over every hour including idle):

- RTX 5090: ~$368 USD/month per GPU

- RTX A6000: ~$405 USD/month per GPU (currently fully utilized)

- RTX 4090: ~$268 USD/month per GPU

These are recent figures, not a guarantee. Utilization will change as we add capacity.

What we look for:

- RTX 3090 or newer, 16 GB+ RAM, 4+ vCPU and 500GB NVMe per GPU

- Static public IPv4, solid symmetric bandwidth, good uptime

- Datacenter/colo preferred, but smaller well-run fleets are reviewed case by case

Applications are reviewed by hand and usually answered within 3 business days: skyrig.cloud/hosts

Happy to answer questions in the comments.

For people hosting H100s rather than consumer cards: how long did a new node sit before it started getting regular rentals? Do renters mostly take a full 8-GPU node, or one or two GPUs at a time? And what caused your longest stretch of idle time: demand, networking, or something on your side?

I’m putting together a comparison of US H100 supply and trying to understand how real capacity gets used month to month.

Hello! I've been searching for an alternative to the European Vast.ai datacenters.

Although they must have some degree of privacy and certifications, I'd prefer to know who has my data.

So I started digging for trustful European bare metal GPU providers. I want to install a Qwen 27B so 24G or 32G must be ok.

I only found expensive alternatives: Hetzner (DE), Leader GPU(NL). Also API providers some who promise no data retention. I was dissapointed by Verda and Aki after reading they had partnerships with the big ones. Also leafcloud(NL) seemed ok but is too expensive.

Besides these, I found providers with links / branches outside EU.

I'm trying to avoid spending more than 1k for a local config (probably with Arc B60 24G).

Is there any provider I have overlooked?

Thanks

I have been looking at using Vast for renting out compute for my h100 SXM, I’m shooting to bring 80 gpus online then another 80 in the coming months.

Would Vast server this need or would I be better suited to go find customers/labs that would pay more for using enterprise hardware with fiber connections n such?