AI & Machine Learning

Gemma 4 on an Amazon SageMaker Endpoint: AWS CLI, NVIDIA L4, and an MCP Server

32 sec read23 views0 listens

The Gemma 4 E2B large language model can now be deployed on an Amazon SageMaker real-time endpoint using NVIDIA L4 GPUs, managed via AWS CLI commands and a Python MCP server. This setup provides a managed HTTPS inference server without requiring manual instance patching or security configuration. The deployment process is detailed step-by-step, offering a robust solution for hosting and managing LLMs on AWS for scalable AI applications. The project's repository provides the necessary tools and instructions for implementation.

Read the full article at DEV Community

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

You Might Like

AI & Machine Learning5 hours ago32 sec read

From APIs to AI Agents: How My Skills Apply to Today's Software World

The author highlights the increasing convergence of software development disciplines, emphasizing that modern applications now routinely integrate APIs, user interfaces, databases, cloud deployment, automation, and AI. This shift necessitates a broad...

Ali Nemati

AI & Machine Learning4 days ago29 sec read

Infrastructure Code Controls Internal DNS Hostnames - Media Release Reconciliation

Managing internal DNS hostnames via infrastructure code requires separating platform-owned zones from customer-owned zones to maintain proper deployment models. For platform-owned zones, the infrastructure repository should store desired state, which...

Ali Nemati

AI & Machine Learning5 days ago30 sec read

Running a serverless AI code review agent on AWS Lambda with PR-Agent and CDK

PR-Agent, an open-source AI code review agent, can now be self-hosted on AWS Lambda using CDK for serverless deployment. This approach allows for cost-effective operation, as the agent only runs when needed for pull requests, and avoids credential ma...

Ali Nemati