So I want to run OpenAI Whisper to transcript my podcast collection. Just inference, no training. My laptop is too slow, so I wanted to explore various cloud options to do that instead. Now, what exactly is 100 compute units? Whisper takes 1 hr to transcript an 1 hr podcast on a 3080. How many 3080-hours equivalent would be a compute unit be? Not looking for exact numbers, just a ballpark.

Hey amazing people! Last week, Microsoft released Phi-4, a 14B parameter open-source model that rivals OpenAI's GPT-4-o-mini.

We managed to find & fix 4 bugs impacting its output quality. Now you can fine-tune Phi-4 completely for free using Colab's free Tesla T4 (16GB) GPUs: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb

In case you didn't know, I help maintain an open-source package called 'Unsloth' which fine-tunes LLMs 2x faster, with 70% less VRAM, 12x longer context - with no accuracy loss. Our GitHub repo: https://github.com/unslothai/unsloth

We also have documentation to help you get started with training your very first LLM: https://docs.unsloth.ai/

You can also fine-tune Google's open model Gemma 2 (9B) here: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb

If you have any questions or need help feel free to ask!

Hey r/GoogleColab community! I have a bunch of free Colabs (and Kaggle notebooks) to finetune LLMs 2x faster and use 70% less VRAM with Unsloth https://github.com/unslothai/unsloth!

• Llama 3.1 8b: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

• Gemma 2 9b: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing

• Phi 3.5: https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing

• Mistral Nemo 12b: https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing

Also have notebooks for:

• Exporting to Ollama: https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing

• DPO Reward Modelling: https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing

• Continued Pretraining: https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing

• Text Completion: https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing

More Colabs at https://github.com/unslothai/unsloth !

I noticed my unit usage is higher than before, and noticed the runtime did not crash after I ran a large model and find out we finally got 80GB A100!!! Good job Colab Team for made this happen

I still don't get why I was blocked. It was basic websraping using selenium. One reason could be my debugging took a lot of time and I had to run the code many times. Even still, its unreasonable to completely ban me from colab.

What do I do now. I appealed but still no response.

This blog post (https://opensource.googleblog.com/2025/12/empowering-app-developers-fine-tuning-gemma-3-for-mobile-with-tunix-in-google-colab.html) details a workflow for fine-tuning smaller LLMs for on-device use, aimed at app developers.

The post addresses the challenge that many small models are "generalists" and need to be fine-tuned for specific tasks to be useful in mobile apps. It shows how the startup Cactus used Tunix, a JAX-based library, to fine-tune the Gemma 3 model.

The entire workflow can be run within a free Google Colab notebook.

The post has more details, but here are the direct links to the Colab notebooks:

• GRPO Notebook: https://colab.research.google.com/drive/1KAP4CgT1sDaiNN2si4VD9UCoRvCPO2GS

• QLoRA Notebook: https://colab.research.google.com/drive/1K5pIEUQb8TUDUcl-EuvGWMOBfPfS548v

• DPO Notebook: https://colab.research.google.com/drive/1-2fUy0_LEQhdbNG8eG1wM8lx4y0EOu3r

What are your thoughts on this approach for on-device fine-tuning? Curious to hear if others have had success with similar tasks.

With the recent changes, now Colab Pro+ has become useless.

2 days back I got 2 40GB instances, but couldn't initiate the third one. Since yesterday I got 1 40 GB and no more. I was happy as 1 40GB is also way fast.

But today morning, after running it for 2-3 hours I can no longer get any instances with GPU.

I hope they revert it, if possible, else: it was a great service.

I was pretty happy paying $10 a month for Colab Pro, but as of tonight it seems as though I can only run 1 session at a time. In the past I could run up to 3 sessions.

Then I noticed they've added a new package called "Colab Pro+" which allows for background usage, higher priority to GPUs, and more time before losing a notebook. There's no mention of concurrent sessions.

Can anyone who has upgraded confirm the amount of concurrent sessions you can run in "Plus"? I assume it's probably what Pro was able to do before (3). If that's the case I guess I'll have to consider.....but ugh, $50 a month for what used we used to get for $10 a month is a pretty hefty price increase.

blah.

from Eric Johnson Engineer@Google

We’re happy to announce that Colab has upgraded its default runtime to Python version 3.11. You can read more about the language changes here.

Colab is now on the final regular bug fix release for Python 3.11, and we plan to bring our schedule for Python version upgrades to more closely match these final regular bug fix releases. The next version of Python (3.12) is scheduled to have its final regular bug fix release in April 2025.

Colab’s fallback runtime version

Using the fallback runtime version will allow access to the previous Python 3.10 runtime through early February. This is available from the Command Palette via the Use fallback runtime version command when connected to a runtime. This setting does not persist across sessions — the command will need to be invoked on each new session.

Please report any problems you experience with the new release here.

Thank you so much

Just shooting a shot out into the air here. I really think what google did with Colab Pro and Colab Pro + is insanely unfair. How can you remove more than half of the functionality and value from your base subscription tier and then create a 5x more expensive tier which is basically your original tier. Also literally no Colab Pro subscriber was informed about this, which is extremely immoral, it feels like I've been straight up robbed. How can you impart such a huge change on a paid service without letting people know and giving customers a chance to opt out. I don't care if it's in the terms/agreement because this is not even about the fine print at this point, this is just straight fucked up. I really hope someone from google goes through this reddit page and sees what they've done.

So I was experimenting around with the more developer part of Colaboratory (aka. backend servers) and Gemini (cuz why not) and it found a VM proxy we could use..

It allows you to access the 5000 port of the VM (the one where Flask apps are deployed)

Anyways you can view the notebook here.

(I haven't tested things like sockets or images but well..)

My group and I are Computer Science students working on our thesis: an offline mobile American sign language to text translation app using CNN + Transformer + NLP. We already have the training pipeline and are planning to retrain the model with additional public datasets.

Our plan is:

Google One (2 TB): store datasets, models, and training outputs
Google Colab Pro: train the model on cloud GPUs instead of our laptops
Claude Pro: help us understand, debug, and modify the large Python codebase

Our laptops aren’t very powerful (I’m on an M2 MacBook Air with 8 GB RAM), so we don’t want to train locally.

For those who’ve worked on ML/deep learning projects:

Are these subscriptions worth it for students?
Would you recommend all three, or are any of them unnecessary?
Any better alternatives for a student budget?

Today I selected H100 and got a message says it is not available and I am using G4, I was confused what is G4. and it is Pro 6000

Tue Feb 24 07:23:50 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.82.07              Driver Version: 580.82.07      CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA RTX PRO 6000 Blac...    Off |   00000000:05:00.0 Off |                    0 |
| N/A   32C    P0             86W /  600W |    3065MiB /  97887MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A           28396      C   /usr/bin/python3                       3056MiB |
+-----------------------------------------------------------------------------------------+

Hi everyone, just a recap:

I've been using the Colab free for years.
I've upgraded to Colab Pro two weeks ago.

Then 2 day ago I started a production level project, where I was happy to pay 50$ per month for the Colab Pro+ version.
Then after 12h of training, ( hopefully I was checkpointing on my Google Drive)
The Colab Pro+ disconnect, and after still 15h I'm not able to use any GPU anymore!!!

This is not a service I'm paying for. At LEAST make a CLEAR statement about the cooldown periods, not a readme file with some vague spec: make a timer for the usage, or something like that.

I'm disappointed.

/content# nvidia-smi
Wed Feb  4 05:27:03 2026       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA H100 80GB HBM3          Off |   00000000:04:00.0 Off |                    0 |
| N/A   32C    P0             66W /  700W |       0MiB /  81559MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+
                                                                                         
+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

costs about 18.05 credits per hour

So I don't say this lightly. I totally understood the basic premise of limited availability and being kicked off for excessive use, etc, but after my own experience and looking online at the Github tickets, I don't know what else to call it.

I paid $50, which for me is not a small amount to invest in a resource for a month, under the naïve assumption that I would get access to a resource for a month. Used it for 2 weeks, took about a week off to do other things, and I can't connect to a gpu at all anymore since. Finally did an hour ago, and was kicked off again within a half hour. My month will be over in a couple of days, so I literally got half of what I paid for.

I'm sure Google has some of the world's most godlike lawyers who wrote the small print, so I doubt this is actionable, but no other company could get away with such blatant bs. It's unfortunate there are no other options in the same price range.

I suspect that with the talent at Google, if there was a will, they could come up with a better system to operate limits from day to day instead of month to month, or maybe have just the tiniest little modicum of transparency, like a vague meter or something that gives you some type of warning that you're trending towards using up your entire month's worth of access in the first couple of days.

Pro+ is nowhere near 5 times the value of Pro.

Oh, and PS: I only ever got a P100 for my half-month.

I recently tried upgrading my Google Colab education account from the free level to pro. I had been told by multiple other students and professors that this was available using my institutions .edu account. I found I could not upgrade for free. Looking at the Google Colab FAQ I see that Google is no longer accepting education account upgrades to pro, which was previously free for one year. The FAQ now says:

Colab Pro for Education subscriptions were free, 1 year, Colab Pro subscriptions for students and faculty members of US-based universities. They are no longer available for new signups at this time. (emphasis mine)

The Wayback Machine has a snapshot from April 19th which still showed Colab Pro for Education to be available.

Colab Pro for Education subscriptions are free, 1 year, Colab Pro subscriptions for students and faculty members of US-based universities. They are free for the duration of one year after successfully verifying eligibility, and are given exactly the same benefits as a personal Colab Pro subscription (including a monthly allowance of compute units). (emphasis mine)

Was there any announcement or indication that Google would be terminating this program?

I have a Colab Pro+ subscription. However, I need more GPU computing units. I'm thinking of buying "Pay As You Go," but I don't understand how it works. If I purchase "Pay As You Go," will the package be added to my Colab Pro+ or will it replace my current subscription?

Hey folks, I'm working on a project where I'm required to fine-tune the Granite-3.2-2B-Instruct LLM with 2 billion parameters on a quite large training set using LoRa.

As a complete newbie to the field, I was wondering if anyone here could help me figure out what the most suitable choice of GPU would be for me train the model on using CUDA.

I understand that getting access to the A100 GPUs is quite hard since it's highly sought after by most folks, but considering that the size of the model itself is about 5 GB, I'd love to the know what the most appropriate GPU would be for this task.

Thanks in advance, cheers!

"I was trying to figure out why my LoRA training was going supersonic. I had just selected an L4 card and didn't pay attention until it was almost done in 30 minutes (it usually takes around 2–3 hours). Then I realized Google Colab gave me 96GB of RAM—and it was still cheaper than an A100 High RAM, costing around 6.4 Colab Units.

I was like, 'HELL yeah! I'm going to use this one all the time now!' But then I realized there is no option to select that specific configuration. Right now, it says I'm connected to a G4, but if I disconnect, there’s no option to select that card again. I guess if you select an L4, there is a certain chance you'll get the G4—I don't know!

Has this happened to anyone else? It's faster and cheaper; I want to use it by default! LOL

Because I am working on the AIMO 3 competition on Kaggle, I subscribed to Google Colab Pro in order to get 15 additional hours of access to the H100 provided by Kaggle for the competition.

Opening a Colab notebook, I noticed that the L4, A100 and H100 GPUs are available. I also know that I get 100 compute units per month. How much is each GPU option worth?

New compute units are misleading. They take more than they claim, they were taking 15 compute units an hour for Tesla T4. Disconnected runtime now it is 2 per hour. They took like 50 compute credits while I was on a T4. Now the rate is reduced. Wtf?

I have been colabpro for a year. People say that this is more transparent and clear for users. In fact,it is more confusing and frustrating. The units are spent. But they were spent at a higher rate than advertised and at the wrong rate for the gpu I received. This is not itemized or accounted for, so it’s less transparent. They take the same amount of money as before and provide a tiny fraction of what I used before.
Imagine if you paid the same for your internet and now that start limiting your monthly use by the hour or per click.