How to get free GPU compute for side projects
GPUs are the expensive part of most AI side projects. Training a small model, fine-tuning an open model or running image generation can cost real money by the hour. But there are more ways to get GPU time for free than most people realise, as long as you match the source to the size of the job and keep a close eye on what is running.
Size the job before you look for credits
Start by working out what you actually need. A side project usually falls into one of three buckets:
- Short bursts, such as running inference on an open model or testing a script. Minutes or a few hours of GPU time.
- A fine-tune or small training run. Several hours on a single GPU, sometimes repeated.
- Ongoing serving, where a model needs to stay available. This is the hardest to keep free.
If you only need to call a model, a hosted inference API with a free tier is usually cheaper and simpler than renting a GPU yourself.
Use monthly free compute first
Monthly allowances are the most useful source for side projects because they refresh. Modal, for example, includes $30 of free compute every month, which covers a fair amount of short GPU jobs if you shut containers down when they are idle.
Serverless GPU platforms charge only while your code runs, which suits bursty side projects well. Design your job so it starts, does its work and exits, rather than keeping a machine warm.
Pick the smallest GPU that fits your model. Many side projects run fine on a mid-range card, and a cheaper GPU stretches a monthly allowance much further than a top-end one. Test on a small slice of your data first, then scale up once you know the job works.
Collect signup and promo credits
Many GPU clouds give new users a small credit, and promo codes from launches and creator campaigns can add more. The catalog includes up to $200 in GPU credits for new Hyperstack users, $50 in GPU credit from Verda through promo codes, and a $20 Thunder Compute credit for US university students.
Some offers ask you to add a small amount of money first. Verda's code, for example, unlocks $50 in credits when you pay $5. Read the claim guide on each offer page so you know what the condition is before you sign up.
General cloud welcome credits can help too, but GPU instances on the big clouds are often limited for new accounts and may need a quota increase. Check that before you plan around them.
Write each credit's expiry date on a sticky note or in your tracker. GPU credits are easy to forget and many expire within weeks.
Look for academic and research grants
If you are a student, teacher or researcher, there are larger grants built for you. Modal lists up to $10,000 in compute credits for academics, and Lambda lists up to $5,000 in cloud credits for researchers. These usually need an institutional email and a short description of the research.
Approval is not guaranteed and the amounts are advertised maximums, but a clear, honest description of the project and its expected compute use gives you the best chance.
It helps to include a rough estimate: which GPU type, how many hours, and what the result will be used for, such as a paper, a thesis or an open dataset. Reviewers are more comfortable approving a request that shows you have thought about cost. If you are a student without your own research project, ask a supervisor or lab whether they already hold credits you can use.
Use hackathons for bigger bursts
Hackathons are a reliable way to get a larger block of GPU credit for a short time. The AMD AI League lists $100 in AMD GPU credits when you join, and several AI hackathons bundle credits from compute partners for participants.
If your side project needs one big training run, timing it around a hackathon you would enjoy anyway can make it free. Just read the event rules on what the credits can be used for.
Prepare before the event opens. Have your dataset cleaned, your training script tested on a small sample, and your environment ready to deploy, so that the credits go on the run itself rather than on setup and debugging.
Startup programs if your project grows
If the side project turns into a company, GPU-focused startup programs open up. The catalog includes a $1,000 GPU credit grant from Runpod for early-stage startups, $5,000 in AI Cloud credits from Nebius, up to $100,000 in GPU credits from Crusoe Cloud, and NVIDIA Inception, which offers preferred pricing and partner cloud credits. All of these have eligibility rules and advertise maximums rather than guaranteed amounts.
Do not register a company just to qualify. Startup programs look for a real product and a team, and a side project with users is a better application than a new company with nothing behind it. Wait until there is something to show.
Do not let a forgotten GPU eat your credits
- Set spending alerts or hard limits on every GPU account.
- Use auto-shutdown or idle timeouts wherever the platform offers them.
- Save checkpoints often so you can stop and resume instead of leaving jobs running.
- Check your running instances before you log off for the day.
Most surprise GPU bills come from instances left running overnight. A two-minute check at the end of a session is worth it.