Using Falcon 40B on GCP Vertex AI
Implementation guide · Falcon · Technology Innovation Institute (TII)
GCP Vertex AI exposes Falcon 40B through model ID falcon-40b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-06-15. Next refresh: weekly.
Quick Start
- 1
- 2Use the GCP Vertex AI SDK or REST API to call
falcon-40b— see the documentation for request format.
Code Examples
pip install google-cloud-aiplatformGOOGLE_CLOUD_PROJECTfalcon-40bFor 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".
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("falcon-40b")
response = model.generate_content("Hello")
print(response.text)Pricing on GCP Vertex AI
Capabilities
About Falcon 40B
Falcon 40B is a leading open-source large language model developed by the Technology Innovation Institute in Abu Dhabi, featuring a causal decoder-only architecture with 40 billion parameters. It stands out with its use of rotary positional embeddings, multi-query attention, and FlashAttention, enhancing its contextual understanding and processing efficiency. Trained on 1 trillion tokens using the enriched RefinedWeb dataset, Falcon 40B excels in various natural language processing tasks, ranging from text generation to language translation and question answering. It supports multiple languages and is open under the Apache 2.0 license, promoting both research and commercial use.