Vast.ai - Cheap GPU Rentals That Actually Work

Vast.ai is a GPU rental marketplace where crypto miners, gamers, and random people with expensive graphics cards rent them out to make money. Instead of AWS owning all their hardware, you're renting some dude's RTX 4090 in his basement - which is why it's dirt cheap and occasionally unreliable as hell.

The Marketplace Reality

Here's how it actually works: People install Vast's host software on their machines, set whatever prices they want, and hope someone rents their GPU. You browse their listings like Craigslist, except for compute power instead of used furniture. When you find a machine that looks decent, you pray it actually works and launch a Docker container.

The whole thing runs on supply and demand. When everyone wants H100s for training, prices spike. When crypto crashes and miners need to pay rent, prices plummet. I've seen A100s go from $0.80/hour to $3.00/hour in the same day depending on who's trying to train what.

Pro tip: Set price alerts because GPU prices fluctuate like cryptocurrency.

Three Ways to Get Screwed by Pricing

On-Demand Instances are supposed to be "guaranteed" but cost the most. They won't get interrupted by higher bidders, but the host can still randomly reboot their machine or lose internet connection. It's the most reliable option on a platform where reliability is relative.

Interruptible Instances are where you bid against other users like some dystopian GPU auction. Bid too low and your training job gets paused every 10 minutes. Bid too high and you're paying on-demand prices anyway. The sweet spot changes hourly and nobody tells you what it is.

Reserved Instances lock you into paying for hardware that might die tomorrow. Great discount if the host keeps their machine online for months. Terrible deal when their mining rig catches fire after week 2.

Security (or Lack Thereof)

You're literally running code on stranger's computers, so yeah, security is interesting. Vast.ai tries to verify hosts and track reliability, but "verified" just means the machine responded to a ping test, not that it won't suddenly disappear.

The Docker isolation is solid - you can't access the host's files or other users' containers. But if you're training on sensitive data, remember that your models are sitting on some random person's SSD. Enterprise customers get dedicated clusters that are basically fancy ways of avoiding the guy mining Dogecoin between your training runs.

Those pricing comparisons look great on paper, but here's what the marketing materials don't tell you. Yeah, Vast.ai works great for AI training - when everything aligns perfectly. Here's what actually happens when you try to use it for real work.

AI Training: Great Prices, Random Failures

Fine-tuning models on Vast.ai can save you thousands compared to AWS. But here's what you'll deal with:

RTX 4090s are perfect for small models - until you discover the host overclocked theirs to mining settings and it thermal throttles after 30 minutes. You'll spend an hour debugging why your training suddenly slowed to crawl, only to realize the GPU is hitting 89°C and downclocking itself.

A100s work great for large models - except when the host's machine randomly reboots at 2 AM because Windows Update kicked in. Yes, some people run A100s on Windows gaming rigs. No, you can't predict which ones.

H100s are amazing when you can actually rent one. The cheap ones ($2/hour) are usually broken, misconfigured, or the host is lying about the specs. The working ones cost $4-6/hour anyway, so your savings aren't as dramatic as advertised.

Docker Setup Hell

Vast.ai's Docker templates sound convenient until you need a specific configuration:

PyTorch templates have CUDA 11.8, but your model needs 12.1. Cue 3 hours of dependency hell trying to upgrade CUDA in a container where half the packages break each other.

The "latest" TensorFlow image is from 6 months ago. You'll end up building your own Docker image anyway, which defeats the point of templates.

Custom Docker uploads take forever and sometimes just fail silently. The error message is "upload failed" with no explanation. Try again and maybe it works the third time.

SSH and Networking Nightmares

The CLI tool is actually pretty good for finding instances, but connecting is where things get interesting:

SSH connections drop randomly on about 20% of hosts. No warning, no reconnect, your screen session is gone. Hope you set up tmux with proper session persistence.

Port forwarding breaks constantly. You set up Jupyter on port 8888, it works for an hour, then suddenly refuses connections. The host probably restarted their router or their ISP changed something.

Some hosts have packet loss that makes everything unusable. The machine shows up as available, specs look great, but you get 15% packet loss making file transfers take forever.

Storage: Assume Everything Will Disappear

Unlike AWS where your EBS volumes persist, Vast.ai storage is ephemeral and hostile:

Interruptible instances delete everything when they're preempted. Set up automatic syncing to S3/GCS every 10 minutes or lose your work. I learned this the hard way at 95% training completion.

Host machines fail and take your data with them. That "reliable" datacenter host? Their SSD died overnight. Your model checkpoints? Gone. Your dataset preprocessing? Gone.

Volume contracts sound good in theory but hosts can still cancel them anytime. You're not guaranteed the same machine or even access to your data if the host decides mining is more profitable.

Production: Buyer Beware

Enterprise features help, but you're still renting random people's hardware:

"Verified" doesn't mean professional. Verified just means the machine passed automated tests. It doesn't mean the host won't randomly decide to mine crypto instead of honoring your rental.

24/7 support means Discord. Good luck getting help at 3 AM when your training job dies. The community is helpful, but it's not enterprise SLA support.

Compliance is a joke for most hosts. Unless you pay for dedicated datacenter-only instances, your sensitive model is running on some gamer's rig with questionable security practices.

Getting started with Vast.ai is a pain in the ass. Here's what you actually need to know to avoid the worst pitfalls.

Account Setup: The Easy Part

Sign up at Vast.ai and add a credit card. That's the only part that works smoothly. Download the CLI tool immediately because the web interface sucks for anything beyond basic browsing.

CLI installation breaks on Mac M1 chips - use the GitHub workaround in issue #47 or you'll waste an hour troubleshooting Python path issues.

Finding Instances That Actually Work

Sorting by price shows you the broken ones first. Here's how to find instances that won't immediately crash:

Filter by reliability score 95%+ minimum. Anything below 90% will die within hours. I've never seen an 85% host stay online for a full training run.

Avoid the cheapest listings. Those $0.15/hour RTX 4090s are either scams, thermal throttling, or about to crash. Budget at least $0.40/hour for consumer GPUs that actually work.

Check the host's hardware setup. Single GPU setups are usually more stable than rigs with 8 cards crammed together. Mining rigs repurposed for ML often have cooling issues.

US and EU datacenters are more reliable than random locations. That $0.20/hour A100 in Kazakhstan probably has 200ms latency and 20% packet loss.

Docker Templates: Outdated and Broken

The pre-built templates are convenient until you need anything recent:

PyTorch template has CUDA 11.8 when you need 12.1. Upgrading CUDA in Docker is a nightmare that'll take 3 hours and break half your dependencies.

TensorFlow images are 6+ months old. Good luck getting the latest features or bug fixes.

Jupyter templates work great - until port forwarding randomly breaks and you lose access to your notebooks. Always set up SSH tunnels as backup.

Build your own Docker images and upload them. Yes, it's slower, but you won't spend half your time fighting dependency conflicts.

Data Management: Paranoid Mode Required

Everything will disappear without warning. Accept this reality and plan accordingly:

Set up automatic S3 sync every 10 minutes using aws s3 sync. Not every hour, every 10 minutes. I learned this at 97% training completion when my instance vanished.

Use screen or tmux religiously. SSH connections drop constantly. If you're not running in a persistent session, you'll lose work.

Test data persistence immediately. Create a test file, reboot the instance, see if it survives. Some hosts wipe storage between sessions.

Volume contracts are risky. The host can cancel anytime and your data disappears. Don't trust them for anything critical.

Pricing Reality Check

The billing updates every few seconds, which is awesome and terrifying:

Set spending alerts at $20 and $50. I once left an H100 running overnight debugging a memory leak. $240 bill in the morning.

Interruptible pricing fluctuates wildly. Your $1/hour A100 can become $3/hour when everyone tries to train models simultaneously. Check prices before starting long jobs.

Hidden costs include data egress if you're downloading large models. Some hosts charge for bandwidth.

When Things Break (They Will)

Your instance will fail. Plan for it:

Keep a list of working hosts you've used successfully. When your current instance dies, you can quickly spin up a replacement.

Always test the GPU immediately with nvidia-smi and a quick PyTorch operation. Don't waste time setting up your environment on broken hardware.

Join the Discord for real-time help. The community knows which hosts to avoid and can help debug weird issues.

File support tickets for obviously broken hardware. You might get refunded, but don't count on it.

Vast.ai works great when you accept its limitations and build fault tolerance into everything you do. Treat it like unreliable infrastructure that happens to be really cheap, and you'll save money without losing your sanity.