Don't Buy a GPU for AI. Use These 4 Free Ones First

Author: Hyperautomation Labs · Watch on YouTube

Video authored by the channel shown, not by IANIA; all rights belong to the creator. Transcript and summary generated automatically; the original-language content is preserved.

Original language: English

Original video: Don’t Buy a GPU for AI. Use These 4 Free Ones First · Channel: Hyperautomation Labs · 04:12 · Watch on YouTube

Topics: AI infrastructure and hardware, AI models, Local and open-source models, Multimodal models

Summary

The video compares four free ways to access GPUs for AI before deciding whether to buy a graphics card: Google Colab, Kaggle T4s, Kaggle TPUs, and Hugging Face ZeroGPU. It reports the hardware, usage limits, setup requirements, and model speeds tested on each platform. The presenter recommends choosing a service based on the task, and notes that all four can be combined at no cost. Buying a GPU may make sense if free usage limits are a recurring problem, data must stay local, or workloads need to run all night.

Key points

Free GPU access is time-limited; each service has its own usage clock and reset rules.
Google Colab is presented as the easiest starting point, with a free-account T4 offering 15 GB of memory and sessions of up to 12 hours when a GPU is available.
On Colab, the tested 8-billion-parameter model ran at 32 tokens per second, while a 14-billion model ran at about 8 tokens per second.
Kaggle offers two Nvidia T4s with 16 GB each, for 30 hours per week and up to 12 hours per session; phone verification is required.
On Kaggle, the presenter measured 13 tokens per second for a 14-billion model and 8.6 for a 32-billion model split across both T4s.
Kaggle’s TPU option provides eight chips with 16 GB each, but requires identity verification, may involve a queue, and does not run Ollama.
Hugging Face ZeroGPU provides access to half of an Nvidia Blackwell card with 48 GB; free accounts receive five minutes of GPU time per day while an app is working.
The recommendations are Colab for a first try, Kaggle T4s for larger chat models, TPU for training and the biggest models, and ZeroGPU for image or video models and sharing demos.
The presenter says a paid GPU may be worth considering if free limits are repeatedly reached, data cannot leave the user’s home, or tasks need to run all night.

Chapters

Start	Chapter	Summary
00:00	Why try free GPUs first	The presenter introduces four free GPU services and explains that their access is limited by usage clocks rather than providing a permanent computer.
00:32	Google Colab and Kaggle T4s	The video demonstrates Colab’s free T4 and Kaggle’s two T4 GPUs, including tested model speeds and practical limits.
02:07	Kaggle TPUs and Hugging Face ZeroGPU	The presenter covers Kaggle’s TPU requirements and queue, then tests Hugging Face ZeroGPU and explains its short daily allowance.
03:09	Which service to choose and when to buy	The video matches each service to common AI tasks, suggests combining the free options, and gives situations where buying a GPU may make sense.
04:12	Closing and resources	The presenter invites viewers to request a comparison page, check beginner guides, and support or subscribe to the channel.

Quotes

Before you click buy, stop. — 00:00

What you get is free GPU time, not a computer you keep forever. — 00:32

And TPUs don’t run Ollama. — 02:38

And you can use all four. — 03:40

If your data can’t leave your house or if it needs to run all night. — 03:40

Concepts and entities

Google Colab, Kaggle, Hugging Face ZeroGPU, Ollama, NVIDIA T4, NVIDIA Blackwell, TPU, JAX, Keras, Persona, Qwen

Full transcript (original language)

00:00 You’ve got a graphics card in your cart. Hundreds of dollars, maybe more than a thousand. All so you can run AI on your own. Before you click buy, stop. Right now, four big companies will lend you a GPU for free. No credit card. You just open a browser. I ran all four on camera. You’ll see what each one runs, how fast, and the real limit on each. Then, you’ll know if you need to buy anything at all. One thing first. What you get is free GPU time, not a computer you keep

00:32 forever. Each one gives you a clock, hours a week or minutes a day. When it runs out, it resets and you go again. Plan around the clock and it costs you nothing. Number one is the easiest, Google Colab. Open a notebook, click runtime, then change runtime type. On a free account, these are your choices. A T4 GPU or a small TPU. The big GPUs are grayed out. Those are for paid plans. Pick T4 and save. I ran one command to see what I got, a Tesla T4 with 15 gigs of memory. Then I installed Ollama and ran an 8

01:03 billion model, 32 tokens a second, faster than you can read. A 14 billion model fits, too, but one T4 runs it at about eight tokens a second. Here is the limit. Google doesn’t say which GPU you’ll get or for how long. Sessions run up to 12 hours when a GPU is free. Use Colab for your first try. Zero setup. Number two gives you the most memory, Kaggle. Verify your phone and every notebook can use two Nvidia T4s, 16 gigs each, 30 hours a week, up to 12 hours at a time. No card. And since September 15,

01:36 the old single GPU is gone. Two T4s is now the only GPU on the menu. Here’s what I measured, an 8 billion model, 32 tokens a second. The 14 billion model that crawled on one T4, 13 tokens a second here. And the 32 billion model split across both cards, 8.6. Both GPUs about 90% full. That model won’t even load on most gaming cards. Two things that bit me. Keep your models inside the working folder or Kaggle warns you about disk space. And stopping any cell stops

02:07 Ollama too. Just start it again. Number three is hiding in the same Kaggle menu. Switch the accelerator to TPU. That’s Google’s own AI chip. Eight of them, 16 gigs each. 128 gigs of memory. 20 hours a week, 9 hours at a time. But two things have changed. First, Kaggle now asks for a quick identity check before it unlocks the TPU. You do it with your phone camera through a service called Persona. Second, you may wait in line. After I switched it on, my session sat in the

02:38 queue for over 20 minutes. And TPUs don’t run Ollama. You use Google’s own tools, JAX or Keras. Use the TPU for training and the biggest models when you can plan ahead. Number four is the newest hardware here. Hugging Face zero GPU. Half of an NVIDIA Blackwell card. 48 gigs. You don’t set anything up. Lots of AI apps already run on it. I opened Gwen’s own image app, signed in with a free account, and typed one prompt. About a minute and a half later, there it was. A laptop and four

03:09 green GPUs. The GPU part took under 30 seconds. The clock is short, 5 minutes of GPU a day with a free account. But it only counts while the app is working. After 30 days, you can host two apps of your own free on a public link. Use it to try the newest models in one click. So, which one? Never run AI before? Start on Colab. Want to chat with a big model, 14 to 32 billion? Kaggle’s two T4s. Training or the biggest models? The TPU. Image and video models or sharing a

03:40 demo? Zero GPU. And you can use all four. That’s 50 hours a week on Kaggle plus daily minutes on Hugging Face plus Colab for zero dollars. When should you buy? If you hit the clock every single week. If your data can’t leave your house or if it needs to run all night. And here’s the bonus. You’ll know exactly how much memory you need because you already ran the models free. I put all four on one page. Each limit, the exact sales I ran and the speeds.

04:12 Comment four GPU all one word and my bot sends it to you. New to cloud code? My beginner guides are in the description. And if you want to back the channel, become a member here on YouTube and a subscriber on Facebook. If this saved you from a checkout page, hit subscribe. It’s free, too.