Using MedLM Large on GCP Vertex AI

Implementation guide · MedLM · Google DeepMind

Serverless

GCP Vertex AI exposes MedLM Large through model ID medlm-large. 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. 1
    Create an account at GCP Vertex AI and generate an API key.
  2. 2
    Use the GCP Vertex AI SDK or REST API to call medlm-large — see the documentation for request format.

Code Examples

Install
pip install google-cloud-aiplatform
API key
GOOGLE_CLOUD_PROJECT
Model ID
medlm-large

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".

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("medlm-large")
response = model.generate_content("Hello")
print(response.text)

Pricing on GCP Vertex AI

Capabilities

Structured Outputs

About MedLM Large

MedLM Large is Google DeepMind's MedLM model focused on medical and clinical reasoning. It is deprecated (originally released 2023-12-01); use it only for reproducing earlier results or evaluating drift over time.

Model Specs

Released2023-12-01
Context32k
ArchitectureDecoder Only

Provider

GCP Vertex AI

Google Cloud Platform (GCP)

Mountain View, California, United States