{
  "id": 9965541,
  "title": "Gemma 4 on an Amazon SageMaker Endpoint: AWS CLI, NVIDIA L4, and an MCP Server",
  "url": "https://urgent.news/2026/09/26/gemma-4-on-an-amazon-sagemaker-endpoint-aws-cli-nvidia-l4-and-an-mcp",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-26T11:10:24.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/gde/gemma-4-on-an-amazon-sagemaker-endpoint-aws-cli-nvidia-l4-and-an-mcp-server-5cdd"
  },
  "original_language": "en",
  "account": null,
  "summary": "This article provides a step-by-step deployment guide for Gemma 4 E2B to an Amazon SageMaker hosted GPU enabled system. The project aims to serve Gemma 4 E2B from a SageMaker real-time endpoint on one NVIDIA L4 GPU using the vLLM container AWS publishes for SageMaker. The article outlines the eight-step deployment process, including setting up the basic environment, installing the required Python packages, configuring the environment variables, and deploying the model using AWS CLI commands. The project also includes a simple MCP (Model Management Control Plane) transport using stdio, where the client launches the server as a local process and communicates with it over stdin and stdout.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}