Free 32GB VRAM for Big AI Models: Kaggle + Ollama, No GPU Needed
Kaggle gives you two free T4 GPUs, Ollama runs the model on them, and a Cloudflare Tunnel lets your own PC use it. This guide uses the latest October 2026 models that fit the setup — Qwen 3.8, gpt-oss, Gemma 4 and more — with the steps, sizes, and honest limits.
You do not need to buy a GPU to try a big AI model. Kaggle gives you two free T4 GPUs, Ollama runs the model on them, and a Cloudflare Tunnel lets your own PC use it. Your laptop only sends the question and shows the answer, so a weak laptop is enough.
One honest correction first: "32GB" here is two separate 16GB T4 cards, not one 32GB card. The model has to fit across both cards, and that split is part of why this is a free trial setup, not a replacement for a real GPU machine.
A second correction matters more: the video demo used older-generation models. The setup steps below are the same, but the models here are the latest ones that fit this setup as of October 2026, checked against the current Ollama library. Do not pull the video's older models when a newer one does the same job better.
How it works, in plain words
Kaggle runs the notebook in the cloud. Ollama downloads and serves the model inside that notebook. The Cloudflare Tunnel gives you a temporary web link to Ollama, so your PC can send prompts to the model running on Kaggle. When the session ends, the model reloads next time.
Set it up, step by step
- Open Kaggle and create a new notebook.
- If the GPU T4 x2 option is greyed out, verify your phone number first. The video shows this fix step by step.
- In the notebook settings, turn on GPU T4 x2 and internet.
- Check the GPUs with !nvidia-smi. You should see two Tesla T4 cards.
- Check disk space with !df -h. Kaggle gives you 20GB of saved space, so keep each model under about 19GB, and keep only one big model at a time.
- Install Ollama with !curl -fsSL https://ollama.com/install.sh | sh, then start the server.
- Check the server with !curl http://127.0.0.1:11434.
Run the latest models first
Start small to prove the setup works, then go bigger. Check what is actually running on the GPU with !ollama ps after each pull. Do not assume both GPUs are being used just because you picked T4 x2.
- !ollama pull gemma4:12b, then !ollama run gemma4:12b "Say hello in one short line" — Google's Gemma 4, 7.6GB, with vision and a 256K context. The fastest proof that the setup works.
- !ollama pull gpt-oss:20b, then !ollama run gpt-oss:20b "Explain gravity in three short lines" — OpenAI's open-weight reasoning model, 14GB, 128K context.
- !ollama pull qwen3.8:27b, then !ollama run qwen3.8:27b "Explain gravity in three short lines" — Alibaba's Qwen 3.8, 18GB, 256K context, the strongest all-round pick here for general work and code.
For longer answers, raise the context with num_ctx, for example 16384 in the API call shown in the video description. Ollama also accepts a Hugging Face GGUF model through its hf.co path, but check the file size first: if it is over about 19GB, it does not fit Kaggle's saved disk.
Pick by job, not by hype
- Everyday chat and vision: gemma4:12b, 7.6GB. Reads images, reasons well for its size, and leaves disk room for a second model.
- Reasoning and agents: gpt-oss:20b, 14GB. Built for reasoning and tool use, and the safest big step up from Gemma 4.
- Best all-round and code: qwen3.8:27b, 18GB. The strongest general pick in this size class in October 2026, with vision and a 256K context.
- Coding specialist: qwen3-coder:30b, 19GB. A coding-tuned mixture-of-experts model with only about 3.3B active parameters per token, so it answers faster than its size suggests. Pull it only after deleting other big models, because 19GB leaves almost no disk headroom.
- Agentic work from Meta: muse-glimmer:30b, 18GB, 128K context. Meta's August 2026 open-weight agentic model, under Apache 2.0, built for tool use and local agents.
Do not pull models that cannot fit: gpt-oss:120b at about 65GB and nemotron-3.5-lightning at about 25GB both exceed Kaggle's 20GB saved disk, so they are not options in this setup, no matter how new they are.
Use it from your own PC
Install Cloudflare Tunnel in the notebook, then start a tunnel to http://localhost:11434. The tunnel log gives you a temporary trycloudflare.com link. From your own PC, send a prompt to that link's /api/generate endpoint with the model name, prompt, and stream: false.
Never share that tunnel link. Anyone with it can use your model while the session is live.
Honest limits
- About 30 GPU hours a week, and one session up to 12 hours.
- Only 20GB is saved between sessions, so models reload next session.
- Free GPUs can queue at busy times, and Kaggle can change the quota.
- Your text goes to Kaggle computers, so never send secret files or private documents.
- Stop the session when you are done, and free disk space with !ollama list and !ollama rm for models you no longer need.
This setup is best for trying big models, testing prompts, and learning, not for running a service that must stay up all day.
Frequently asked questions
Is Kaggle's free GPU really free?
Yes, with a phone-verified account and no card needed for this setup. The free part is limited: about 30 GPU hours a week and sessions up to 12 hours, so it is a trial and learning setup, not unlimited compute.
Is 32GB VRAM one big GPU?
No. It is two separate 16GB T4 cards. A model has to be split across both cards, so check with ollama ps that the model is actually running on the GPU before you judge its speed.
Do I need a powerful laptop?
No. The model runs on Kaggle computers in the cloud. Your laptop just sends the question and shows the answer, so a weak laptop is enough for this setup.
Why are the video's models not in this guide?
Because they are the older generation. The video's setup steps still work, but this guide replaces its models with the latest ones that fit the same Kaggle disk and VRAM limits as of October 2026: Gemma 4, gpt-oss, Qwen 3.8, Qwen 3-coder, and Muse Glimmer.
Can I run ChatGPT or Claude this way?
No. Claude, ChatGPT, and Gemini are API only and cannot be downloaded. You can run open models from their makers, such as gpt-oss:20b, gemma4:12b, and qwen3.8:27b, in the same setup.
Is my prompt private on this setup?
Not fully. Your text goes to Kaggle computers, and anyone with your tunnel link can use your model. Never send secret files or private documents, and never share the tunnel link.
Watch the demo, then try one latest model
To see the phone-verification fix, the GPU proof, and the tunnel link on screen, watch the companion video: Free 32GB VRAM for Big AI Models: No GPU Needed. Use its steps, but pull the latest models listed above, not the older ones shown on screen.
Watch the full video on YouTube
Start with gemma4:12b. Once ollama ps confirms it is on the GPU, move to qwen3.8:27b for your real work.
Related Articles
