AI / Google Antigravity Gemini Fundamentals Interview Questions
What are Gemma models and how do they relate to the Gemini API?
Gemma is Google's family of open-source, lightweight language models built from the same research and technology as Gemini. They are available for free download and can be run locally or deployed on your own infrastructure without the Gemini API.
| Aspect | Gemma | Gemini |
|---|---|---|
| Open source | Yes - weights available on Hugging Face/Kaggle | No - closed model, API-only |
| Hosting | Self-host anywhere | Google's infrastructure via API |
| Size | Compact (2B to 27B parameters) | Much larger frontier models |
| Privacy | 100% on-premise possible | Data goes to Google's servers |
| Cost | Compute cost only (no per-token fee) | Pay per token |
| Capability | Strong for size; not frontier | State-of-the-art frontier |
| Updates | New releases periodically | Continuous via API |
# Gemma 4 models (latest as of mid-2026): # gemma-4-26b-a4b-it (26B instruct) # gemma-4-31b-it (31B instruct) # Available on AI Studio and via Gemini API for inference # Also available for local use via: # Hugging Face: from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("google/gemma-4-9b") # Ollama: # ollama run gemma3 # Google also makes Gemma available through the Gemini API: # This lets you use the API interface without leaving Google's ecosystem response = client.models.generate_content( model="gemma-4-26b-a4b-it", # Gemma via Gemini API contents="Explain gradient descent.", )
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