Google’s November 2025 VS Code extension eliminates the “I don’t have a GPU” excuse by bringing Colab’s free NVIDIA T4 GPU directly into your local editor. This tutorial shows you how to set it up in under 5 minutes and explains what you get — plus the critical limitations you need to know about Google’s terms of service before relying on “their compute” for production work.
What You’re Actually Getting
Free Tier: NVIDIA T4 GPU (15GB usable VRAM) or TPU v5e, with the same usage limits as browser-based Colab. Sessions auto-disconnect after 12 hours of activity or 90 minutes of idle time, and GPU availability is not guaranteed—you might get a slower K80 during peak hours. Free users cannot run background tasks or persistent servers.
Colab Pro ($12.99/month): Access to premium GPUs like A100 (40GB) and L4 (24GB), longer runtimes up to 24 hours, background execution, and priority access during resource constraints. Pro+ ($55/month) adds even more compute units and eliminates most queue times.
Step 1: Install the Google Colab Extension
- Open VS Code and press Ctrl+Shift+X (Windows/Linux) or Cmd+Shift+X (Mac) to open Extensions
- Search for “Google Colab” and install the official extension from Google
- Restart VS Code to activate the extension
- Click the Google account icon in the bottom-right status bar to sign in with your Google account
Step 2: Connect a Notebook to Colab Runtime
- Create or open a
.ipynbJupyter notebook file in VS Code - Click the kernel picker in the top-right corner (shows current Python environment)
- Select “Connect to a Colab Runtime” from the dropdown
- Choose “New Colab Server” when prompted
- Select your desired accelerator: T4 GPU (free), TPU v5e (free), or premium options if on Pro
- Wait 30-60 seconds while Colab provisions the virtual machine
Verification: Run !nvidia-smi in a notebook cell. If successful, you’ll see “Tesla T4” in the output instead of your local GPU. Your local environment is now connected to Google’s cloud hardware.
Step 3: Install Packages on the Remote Runtime
The Colab runtime starts fresh each session—packages installed locally don’t transfer automatically. Install dependencies directly in notebook cells:
!pip install transformers torch accelerate
!pip install pandas numpy scikit-learn
Pro Tip: Create a requirements.txt file and install everything at once: !pip install -r requirements.txt. Alternatively, mount Google Drive to persist installed environments across sessions.
Step 4: Load Your Data and Run GPU-Accelerated Code
Upload files via VS Code’s file explorer—they appear in /content/ on the Colab runtime. For larger datasets, mount Google Drive for direct access:
from google.colab import drive
drive.mount('/content/drive')
# Access files: /content/drive/MyDrive/your-folder/data.csv
Now run GPU code exactly as you would locally. The T4 provides 16x speedup over CPU for model training—7B parameter models that crawl at 2 tokens/sec on laptops run at 50+ tokens/sec on the T4.
Understanding the Compute Units System
Colab introduced “Compute Units” (CUs) in 2024 to limit free usage. Free users receive a weekly quota that refreshes every 7 days, but Google doesn’t publish exact CU allocations. Intensive workloads—like training transformers or running inference on large models—deplete CUs faster than simple data analysis. Once exhausted, you wait for the weekly reset or upgrade to Pro.
What burns CUs fastest: GPU usage (especially A100/L4), TPU sessions, long-running notebooks, and memory-intensive operations. Simple tasks like data cleaning or visualization consume minimal CUs even on GPU runtimes.
Critical TOS Limitations You Must Know
Prohibition #1: Cryptocurrency mining. Any crypto-related compute—mining, validation, proof-of-work—results in immediate account termination without warning or appeal. This is the most aggressively enforced restriction.
Prohibition #2: Automated scraping and mass downloads. Colab detects and blocks systematic data extraction, bulk file downloads, and bot-like behavior patterns. Web scraping small datasets is fine; automating thousands of requests triggers bans.
Prohibition #3: Production deployments and persistent services. Free tier explicitly prohibits hosting APIs, web servers, or always-on applications. You cannot use Colab as infrastructure for services others depend on — only for interactive development and prototyping.
Data ownership concerns: Google’s Terms of Service grant them rights to analyze usage patterns and potentially access notebook contents for service improvements. If your work involves proprietary algorithms or sensitive data, understand that “their compute” means accepting their oversight. Competitors using the same platform theoretically could reverse-engineer approaches Google implements based on aggregate user patterns.
Common Troubleshooting Issues
“Can’t connect to runtime”: Check you’re signed into the correct Google account. Sign out and back in through VS Code’s account menu (bottom-right). Verify your account isn’t flagged for TOS violations by opening browser-based Colab.
“Session disposed after idle”: Colab terminates sessions after 90 minutes of no code execution. Keep the session alive by running a cell periodically or upgrade to Pro for background execution. Free tier does not support unattended training.
“Import errors despite installing packages”: VS Code’s IntelliSense checks your local Python environment, not the remote Colab runtime. Red squiggles for installed packages are cosmetic—code will still execute correctly. Disable pylint warnings or configure VS Code to check the remote interpreter.
“Out of memory errors on T4”: The T4’s 15GB VRAM limits model sizes. Use quantization (4-bit/8-bit), gradient checkpointing, or smaller batch sizes. Models exceeding 7B parameters often require Pro-tier A100s with 40GB memory.
When to Pay for Colab Pro vs. Alternatives
Stick with free Colab if: Your workloads finish within 12-hour sessions, you can tolerate occasional GPU unavailability, and weekly CU quotas suffice for your training frequency. Perfect for students, hobbyists, and infrequent model experimentation.
Upgrade to Pro ($13/month) if: You need reliable access during peak hours, require A100-class GPUs for larger models, or run multi-hour training jobs that exceed free tier limits. The background execution alone justifies the cost if you train overnight.
Consider alternatives if: You need persistent infrastructure (APIs, web services), guaranteed compute without preemption, or want to own your environment. Kaggle offers 30 free GPU-hours weekly with 9-hour sessions. Thunder Compute provides on-demand T4s at $0.27/hour—cheaper than Colab Pro for occasional intensive work. AWS, Azure, and GCP offer enterprise-grade reliability but at 5-10x the cost.
The Ownership Trade-Off
As one commenter noted, “If you don’t own the compute then you don’t own your product.” This isn’t paranoia—it’s infrastructure reality. Google can change TOS, raise prices, or terminate services with minimal notice. Projects built assuming free GPU access face existential risk if Colab becomes uneconomical or restricted. Stable Diffusion’s popularity reportedly triggered Colab’s shift from unlimited free usage to the current CU system, demonstrating how user behavior shapes platform policies.
For prototyping, education, and personal projects, Colab’s free tier is unmatched. For production systems, business-critical training, or proprietary research, investing in owned infrastructure—whether local GPUs, rented bare-metal servers, or dedicated cloud instances—provides independence that “their compute” can never offer. Use Colab to prove concepts, then migrate to owned resources before dependencies become irreversible.
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