How I Fine-Tune LLMs at Hackathons on Rented Cloud GPUs

My Laptop Doesn't Train the Model. A Rented H100 Does.

I rent the GPU.

People ask me how I fine-tune LLMs and other big models during a hackathon when their own laptops take all weekend. I don't train on my laptop. I rent an A100 or H100 by the hour on Vast.ai, train there, copy the weights back, and shut the machine down.

That part isn't new. What changed is who runs the machine. These days an AI agent rents it, sets it up, watches it, and fixes it when it breaks. The best proof I have isn't even a training run. It's a 4K music video an agent rendered on 11 rented GPUs, driven from one terminal on the same PC I game on.

Why a Rented GPU Beats the Other Options

I considered my options. Your laptop is out: a fine-tune that needs hours on an H100 won't finish on a laptop before judging, and the laptop is busy training instead of running your demo. Free notebooks are fine for small experiments, but you don't control which GPU you get or how long the session lasts. The big clouds often want a GPU quota increase first, and that approval can take longer than the hackathon.

What's left is a marketplace where you rent someone else's idle GPU by the hour. That's Vast.ai. Billing is per second, so a two-hour run costs two hours.

When I wrote this, Vast's own pricing pages listed H100s at a 30-day median of about $1.33 to $1.87 an hour. For comparison, the agent's RTX 5090s ran about $0.55 to $0.75 an hour each.

The Agent Does the Babysitting Now

Before agents, renting a GPU meant I babysat it. SSH in, install drivers, start training, then keep checking back to see if it crashed. Now I hand an agent an API key, a budget, and a goal, and go build the frontend.

Here's what that looked like on the music video. Claude, running in a terminal on my PC, did every step through Vast's API with plain curl and ssh, no web console:

The rent call itself is short:

{"client_id": "me", "image": "vastai/base-image:@vastai-automatic-tag", "disk": 80,
 "runtype": "ssh_direct", "env": "-e NVIDIA_DRIVER_CAPABILITIES=all"}

Every Failure Was a Number That Stopped Moving

With no screen to look at, every problem first showed up as a number that didn't move: a GPU at 0%, a frame count stuck, a file growing too slowly. The agent polled those numbers on every machine and caught each problem within minutes.

GPU at 0% with the job running. That host's driver never gave Blender a GPU context. The agent destroyed it, rented a replacement, and now checks every GPU is busy a minute after starting.

A build that "succeeded" with no output. Blender exits 0 even when its Python script throws. The fix was to grep every log for a traceback and retry.

A file arriving at 0.2 MB/s. That host's upload link was just slow. Re-rendering its scene on a fast machine took 25 minutes, instead of waiting hours.

Here's the surprise. Two render workers per GPU left the GPU half idle, because each 1080p frame needed about 3 seconds of single-threaded CPU work before 1.2 seconds on the GPU. Running five workers per GPU made the render about three times faster. Training has the same trap: a GPU stuck well under 100% usually means the CPU side, like data loading, can't keep up.

The agent also checked its own work. A QA pass inspected every scene of the first 4K file, then one fix agent per scene repaired its findings, testing each fix with renders on the rented machines instead of my PC.

Paste This Prompt Into Your Agent

For a fine-tune, the job is simpler than a 9,400-frame render: one machine, one training run. Put your Vast API key in an environment variable, add a little credit, and give your agent something like this:

My Vast.ai API key is in VAST_API_KEY. Spend only the credit already on the
account. Never add funds.

Before you start, ask me: which base model to fine-tune, where the dataset
lives, and what "done" looks like (epochs, eval metric, output format).

Then:
- Check my credit through the Vast REST API.
- Rent one verified, on-demand H100 (or A100 80GB) with reliability 0.98+.
- Attach my SSH key to the instance and confirm you can log in.
- Install what training needs, copy the dataset over, and start training
  with setsid nohup so it survives a dropped connection.
- Every few minutes, check GPU utilization, the loss, and the log. If the
  GPU sits at 0% for a minute, read the log and fix it or replace the box.
- When training finishes, copy the weights back, verify the files, then
  DESTROY the instance and tell me the total cost.

Spend the Saved Hours on the Demo

The real win isn't the cheaper GPU. It's that training runs in the background while you build the frontend and practice the pitch, and nobody on the team spends hour 20 watching a loss curve. All three render runs for the music video cost about $18 together. A fine-tune on one H100 for a few hours costs less than that.