Students and self-learners in Vietnam often search for "thuê RTX 3060 giá rẻ", meaning a cheap RTX 3060 to rent, because it is the obvious first step into GPU computing: enough memory to learn deep learning properly, without paying for a card built for production. This guide explains what a 12 GB RTX 3060 can and cannot run, how it compares with two nearby options, how to find a low price near Vietnam on Cloudj, and the habits that keep a learner's bill small.
If price is your main concern, the cheapest GPU near Vietnam guide covers the cost side in more depth.
What 12 GB of VRAM gets you
The RTX 3060 is an Ampere consumer card with 12 GB of memory. For learning, that is a comfortable amount. Rough guidance, not benchmarks:
| Task | Fits on 12 GB? |
|---|---|
| Course notebooks: CNNs, small transformers, classic PyTorch tutorials | Yes, comfortably |
| Fine-tuning BERT-size models for classification on Vietnamese text | Yes |
| Running a 7B-8B language model quantized to 4 bits | Yes, with modest context |
| Stable Diffusion 1.5 image generation | Yes |
| SDXL generation | Possible with memory-saving options, slower |
| LoRA training of a 7B model with QLoRA | Possible with small batches |
| Training or serving anything 13B and above at full precision | No |
If your assignments start hitting "CUDA out of memory" regularly, that is the signal to move up a card, not to fight the limits.
RTX 3060 vs 3060 Ti vs 4060 Ti
Three entry cards often sit near each other in price. Their memory differs more than their names suggest.
| Card | VRAM | When to pick it |
|---|---|---|
| RTX 3060 | 12 GB | Default starter: more memory than the Ti |
| RTX 3060 Ti | 8 GB | Faster in compute but less memory; fine for small vision models |
| RTX 4060 Ti | 16 GB | Newer Ada card with more memory, useful for SDXL or larger quantized models |
For learning, memory usually matters more than raw speed, which is why the plain 3060 is often a better learner's card than the 3060 Ti. Check live "from" prices on each model page.
Finding a low price near Vietnam
On Cloudj, GPUs come from a live list of independent host machines, and you choose location by filtering the list by country.
- Open the RTX 3060 deploy page.
- In the Country field, enter
SG, HK, VN. Cloudj has no own data centre in Vietnam and machines there are not guaranteed to be listed, so Singapore and Hong Kong are the normal choices. - Set a small disk, enough for your environment, datasets and outputs. It cannot be resized later, and a bigger disk costs more.
- Look at the results, sorted by total price, lowest first.
If nothing is listed, add TW or JP, or clear the country field. For coursework, latency hardly matters: you are running notebooks, not serving customers. Stock changes during the day, so try again later if needed.
Use the Jupyter template
For learners, the "PyTorch + JupyterLab" template is the easiest start. JupyterLab can be opened from the instance page when the console offers it; a session lasts 12 hours, after which you open it again. Notebook traffic passes through Cloudj, so for large files use scp or rsync over SSH instead. You still need an SSH key selected at deploy, because GPU instances use SSH keys only.
A quick first check in a notebook cell:
import torch
print(torch.cuda.is_available(), torch.cuda.get_device_name(0))The Jupyter on a GPU for students article walks through the notebook workflow in detail.
Habits that keep a student's bill small
GPU instances are billed per minute from a prepaid balance while they run. That rewards discipline.
- Prepare offline. Write and debug code on your laptop with a tiny dataset. Rent the GPU only for the parts that need it.
- Download results, then destroy. Destroy deletes the instance and its disk and stops all charges. Copy notebooks and outputs off first:
rsync -avP -e "ssh -p <port>" root@<host>:/workspace/ ./my-course-work/- Use Stop sparingly. Stop keeps the disk but still charges storage, and it releases the GPU. When you start again, it may wait until that GPU is free. For a learner, destroy-and-redeploy is often simpler. The stop vs destroy guide explains the trade-off.
- Keep the deposit in mind. Deploying holds a deposit from your balance; the unused part returns after you destroy, sometimes after up to 72 hours.
Students in Vietnam can top up the balance by bank transfer in VND. New accounts start at the lowest account level, which already includes consumer GPUs like the 3060.
Where to go next
Once 12 GB feels tight, the natural steps are a 16 GB card for image work or a 24 GB card for larger language models. For a Vietnam-focused overview of all options, see GPU cloud for Vietnam.
FAQ
Is an RTX 3060 enough for a machine-learning course?
For most courses, yes. 12 GB handles typical notebooks, small transformers and quantized 7B models.
Should I pick the 3060 Ti because it is "Ti"?
Not for learning. It has 8 GB, less than the plain 3060's 12 GB, and memory is usually the limit you hit first.
Can I rent a GPU located in Vietnam?
You can try VN in the Country filter, but machines in Vietnam are not guaranteed. SG and HK are the usual alternatives.
What happens if I forget to destroy the instance?
It keeps charging per minute while running. Destroy it when you finish each session.
Start learning on a 3060
Open the RTX 3060 deploy page, enter SG, HK, VN and pick the lowest-priced machine that suits you. Live prices for every entry card are on GPU cloud.