Best Google Colab alternatives, ranked by what you actually get for free#

Colab was my starting point too. It is easy to open, and then it disconnects in the middle of a run and your files are gone. I started keeping a list of where else I could get a free GPU, and how each one actually behaves when you try to use it for more than a demo.

I rank these on three things: how many free GPU hours you get, how stable those hours are, and how fast you can go from signup to a running notebook.

How I rank them#

Criterion	What it measures
Free GPU hours	Total GPU hours provided per week or month
Quality of hours	Disconnection frequency, session stability
Ease of use	Setup complexity, interface quality, time to first run

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Tier list summary#

S tier, the ones I actually rely on#

Platform	GPU	Free hours	Key advantage
Kaggle	Tesla P100	30 hrs/week	Reliable, no disconnections, 4 CPUs + 29 GB RAM
AWS SageMaker Studio Lab	T4	4 GPU hrs/day	Persistent storage, rarely disconnects
Lightning AI	T4, L4, A10G	22 hrs/month	VS Code interface, never disconnects once connected

A tier, very good with one catch#

Platform	GPU	Free hours	Key advantage
Paperspace Gradient	M4000	~6 hrs/session	Persistent storage, no re-uploading data

B tier, decent but flawed#

Platform	GPU	Free hours	Key limitation
Google Colab	T4	~4 hrs/day	Vague GPU limits, disconnects after 2-3 hrs, no persistent storage
Saturn Cloud	T4	30 hrs/month	Old interface, frequent disconnections
GitHub Codespaces	CPU only	60 hrs/month	No GPU available

C tier, not what I would pick#

Platform	GPU	Free hours	Key limitation
Azure ML Notebooks	Varies	$20 free credits	Not always free, complex setup
Google Vertex ML Notebooks	Varies	$300 free credits	Requires Google Cloud account, not truly free
Hugging Face Spaces	CPU only	Unlimited	No GPU on free tier, 2 CPUs + 16 GB RAM only

D tier, skip it#

Platform	GPU	Free hours	Key limitation
Deepnote	None	Unlimited	Only 2 CPUs + 5 GB RAM, unusable for ML

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What each platform is actually like#

Google Colab (B tier)#

Colab never tells you exactly how much GPU time you have left. It depends on load. Sessions can run 12 hours on paper, but mine usually dropped after 2 to 3 hours. When it drops, your files go with it because there is no persistent storage. If you already have a Google account it is easy to start. I use it for quick experiments, not for anything I need to finish.

Paperspace Gradient (A tier)#

Disconnects about as often as Colab in my experience, and it never states a clear hour limit. The difference is persistent storage, so you do not re-upload your dataset every time. The free GPU is an M4000, which is weaker than a T4. Note that notebooks are public by default, so check the setting if your work is private. Better than Colab for one reason: your files stay put.

Kaggle (S tier)#

30 hours a week, stated up front, no guessing. The GPU is a Tesla P100, roughly on par with a T4, with 4 CPUs and 29 GB RAM. Sessions mostly stay up. The interface feels like Colab. There is no persistent storage, so I download and upload data each session. Owned by Google. Still the most generous quota I have found.

AWS SageMaker Studio Lab (S tier)#

4 GPU hours plus 12 CPU hours a day, on a T4 like Colab. It rarely drops, storage persists, and it runs most of what I throw at it. You have to register on the AWS site first, which takes a few minutes. Slightly fewer GPU hours than Kaggle, but the storage makes up for it.

Saturn Cloud (B tier)#

The free plan gives you either 64 GB RAM or 1 T4 plus 16 GB RAM. The interface feels dated and it drops more than I would like. The raw resources are fine, the experience around them is not.

Deepnote (D tier)#

2 CPUs and 5 GB RAM, no GPU. You see it advertised a lot. It cannot do ML work at this size, so I stopped trying.

Lightning AI (S tier), my favorite for real work#

22 GPU hours a month on the free tier, plus one studio with 4 CPUs that stays free for CPU use. You add a GPU to the studio with one click. The interface feels like VS Code, and once I am connected it stays connected. It supports T4, L4, and A10G. Phone verification is required, no credit card. This is the one I open when I want a normal dev environment, not just a notebook.

Azure ML Notebooks (C tier)#

This is $20 in credits on signup, not a free tier that stays free. Setup is fiddly and the credits run out. I found no reason to pick it over the free options above.

Google Vertex ML Notebooks (C tier)#

Same story as Azure. It needs a Google Cloud account and the $300 credit runs out. Not free in any lasting sense.

Hugging Face Spaces (C tier)#

No GPU on the free tier, just 2 CPUs and 16 GB RAM with unlimited runtime. Fine for small CPU models and demos. Not where I would train.

GitHub Codespaces (B tier)#

Two CPU options, 2-core with 8 GB RAM or 4-core with 16 GB RAM, 60 hours a month, VS Code in the browser with a terminal. Good for smaller models and general coding. No GPU, so it caps what you can do for ML.

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What changed by 2026#

Kaggle#

Now dual T4 GPUs (30 GB combined), up from a single P100. Still 30 hrs a week, 9-hour session limits. 73 GB persistent storage was added since the older guides. No internet during GPU sessions, which is a security measure. Still my S tier pick for quota size.

Lightning AI#

Now 80 free GPU hours a month, up from 22. T4, L4, A10G on free, with A100 and H100 on paid tiers. 50 GB persistent storage. Free studios restart every 4 hours. Supports VS Code extensions, Cursor, SSH, and CLI. Still S tier, and more generous than before.

Google Colab#

About 15 to 30 hours a week on T4 or P100, 12-hour session limits, still random drops. Colab Pro ($9.99/mo) and Pro+ ($49.99/mo) exist if you want to pay. Still B tier for me. Convenient, just not steady.

Paperspace Gradient#

Free tier is M4000 with 8 GB VRAM. Queue times can get long. Now part of DigitalOcean. Still A tier, and storage is still the reason.

New platforms worth knowing (2026)#

Platform	GPU	Free tier	Notes
Modal	T4/A10G	$1 free, $29 more after adding a payment method ($30/mo Starter)	Serverless GPU, pay-per-second
Thunder Compute	RTX A6000/L40	Student credits	Low-cost dedicated GPUs
Groq	Custom	Free inference	LLM inference only, no training
Cerebras	Custom	Free inference	Fast LLM inference
Hugging Face ZeroGPU	H200 (limited)	Community grants	Demo hosting, must apply

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How I split work to get the most free GPU#

1. Kaggle first, 30 hrs a week that I can count on.
2. Lightning AI for the actual dev environment, 80 hrs a month.
3. Google Colab for quick tries and overflow.
4. Groq or Cerebras for free LLM inference, not for training.

Across platforms that adds up to about 150 free GPU hours a month.

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What I take away from this#

• Kaggle is the most generous and the most steady free GPU I have used.
• Lightning AI is where I do the work, because the VS Code setup feels normal.
• Colab is fine for a fast test, not for a long training run.
• Persistent storage matters more than it looks. Losing your dataset on every drop gets old fast.
• Combined free tiers cover DistilBERT, GPT-2 small, ViT, and 7B fine-tunes with QLoRA without paying.