Using Gemma 2 9B Instruct on Replicate API
Implementation guide · Gemma 2 · Google DeepMind
Replicate API exposes Gemma 2 9B Instruct through model ID google-deepmind/gemma2-9b-it. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-04-19. Next refresh: weekly.
Quick Start
- 1
- 2Use the Replicate API SDK or REST API to call
google-deepmind/gemma2-9b-it— see the documentation for request format. - 3
Code Examples
pip install replicateREPLICATE_API_TOKENgoogle-deepmind/gemma2-9b-itReplicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
import replicate
# reads REPLICATE_API_TOKEN from env
# google-deepmind/gemma2-9b-it format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"google-deepmind/gemma2-9b-it",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Pricing on Replicate API
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.10 |
Capabilities
About Gemma 2 9B Instruct
Gemma 2 9B Instruct, developed by Google, is a state-of-the-art large language model based on the advanced Gemini framework. It is a decoder-only transformer model with 9 billion parameters, offering a balance between size and performance. The model is trained on an expansive dataset comprising 8 trillion tokens, including web documents, code, and mathematical text, a notable 30% increase from its predecessor, Gemma 1.1. This allows it to adeptly handle diverse tasks such as question answering, creative writing, coding, and mathematical problem-solving.