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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.

Gemma vs Gemini
AspectGemmaGemini
Open sourceYes - weights available on Hugging Face/KaggleNo - closed model, API-only
HostingSelf-host anywhereGoogle's infrastructure via API
SizeCompact (2B to 27B parameters)Much larger frontier models
Privacy100% on-premise possibleData goes to Google's servers
CostCompute cost only (no per-token fee)Pay per token
CapabilityStrong for size; not frontierState-of-the-art frontier
UpdatesNew releases periodicallyContinuous 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.",
)

What is the key reason an enterprise might choose Gemma over the Gemini API for their AI workloads?
How are Gemma 4 models accessible via the Gemini API?

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