AI/MLIntermediate

Deploy Large Language Models on GPU

Learn how to deploy and optimize Large Language Models on GPU infrastructure with Float16.cloud. Master techniques for efficient inference, scaling, and cost optimization.

Float16 Team
2 chapters

What You'll Learn

  • Deploy LLM models on GPU infrastructure
  • Optimize inference performance
  • Implement efficient batching strategies
  • Monitor and scale GPU workloads

Deploy Large Language Models on GPU

Welcome to this comprehensive course on deploying Large Language Models (LLMs) on GPU infrastructure. In this course, you'll learn everything you need to know to efficiently deploy and scale LLM applications.

What You'll Learn

By the end of this course, you'll be able to:

  • Deploy popular LLM models like GPT, LLaMA, and others on GPU infrastructure
  • Optimize inference performance for production workloads
  • Implement efficient batching and caching strategies
  • Monitor resource usage and implement auto-scaling
  • Reduce costs while maintaining high performance

Course Overview

This course is divided into 4 comprehensive modules:

Module 1: Introduction to LLM Deployment

Understanding the fundamentals of LLM deployment is crucial for success. We'll cover the architecture of modern LLMs, GPU requirements, and Float16.cloud platform capabilities.

Module 2: Setting Up Your Environment

Get hands-on experience setting up your deployment environment with our platform and tools.

Module 3: Model Deployment

Learn practical deployment techniques including model loading, optimization, and batching strategies.

Module 4: Production Best Practices

Master production-ready deployment strategies including monitoring, auto-scaling, and cost optimization.

Prerequisites

Before starting this course, you should have:

  • Basic understanding of machine learning concepts
  • Python programming experience
  • Familiarity with PyTorch or TensorFlow
  • Experience with command-line tools

Who Should Take This Course?

This course is perfect for:

  • ML Engineers looking to deploy LLMs
  • DevOps engineers managing AI infrastructure
  • Data Scientists moving models to production
  • Technical leads planning LLM deployments

Course Materials

All course materials include:

  • Step-by-step tutorials
  • Hands-on coding exercises
  • Real-world project templates
  • Access to Float16.cloud GPU resources
  • Community support forum

Certification

Upon completion, you'll receive a certificate demonstrating your proficiency in LLM deployment on GPU infrastructure.

Ready to Start?

Let's begin with Module 1: Introduction to LLM Deployment.

Course Chapters

1

Introduction to LLM Deployment

Learn the fundamentals of deploying Large Language Models, understand LLM architecture, and explore GPU requirements for inference.

4 min read
30 min
2

Setting Up Your Environment

Learn how to set up your development environment, create a Float16.cloud account, and configure your first GPU deployment.

4 min read
45 min

Ready to Start Learning?

Begin your journey with 2 comprehensive chapters.

Start Chapter 1