Last refreshed 2026-06-15. Next refresh: weekly.
Why use Gemma 2B Instruct on GCP Vertex AI?
GCP Vertex AI offers Gemma 2B Instruct with pay-as-you-go pricing at $0.04/1M input tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Gemma 2B Instruct across 7 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")gemma-2b-itRequest example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("gemma-2b-it")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Gemma 2B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.10 | $0.10 |
| GCP Vertex AI | $0.04 | $0.12 |
| Cloudflare Workers AI | — | — |
| NVIDIA NIM | — | — |
| Alibaba Cloud PAI-EAS | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.04 |
| Output tokens | $0.12 |
Capabilities
About Gemma 2B Instruct
Gemma 2B Instruct is a large language model developed by Google, designed to balance performance and accessibility with its 2 billion parameters. Derived from the Gemini family, it excels in tasks such as text generation, code interpretation, and mathematical problem-solving. Built on a transformer decoder architecture, it features multi-query attention, RoPE, GeGLU activations, and RMSNorm. Trained on approximately 6 trillion tokens, including web documents, code, and mathematical content, it uses SFT and RLHF for instruction-tuning. Notable for its lightweight design permitting deployment on consumer-grade hardware, it's open-source and optimized for dialogue applications.