Serverless GPU vs Dedicated Instances
Learn the differences between serverless GPU computing and dedicated GPU instances. Understand when to use each approach for your AI/ML workloads.
What you'll learn
- Understand serverless GPU computing
- Learn dedicated GPU instance benefits
- Compare costs and performance
- Build a decision framework
- Apply to real-world AI workloads
Course Content
Introduction to GPU Computing Models
Understanding the fundamental differences between serverless and dedicated GPU computing - the two main paradigms for accessing GPU resources.
Serverless GPU Deep Dive
Understanding how serverless GPU works - the technology, cold starts, pricing models, auto-scaling, and when it's the right choice.
Dedicated GPU Instances Deep Dive
Understanding dedicated GPU instances - full control, predictable performance, and when reserved GPU capacity makes economic sense.
When to Use Serverless vs Dedicated GPU
A comprehensive decision framework for choosing between serverless and dedicated GPU - cost analysis, use case mapping, and practical guidelines.
Float16 GPU Options
Explore Float16's serverless and dedicated GPU offerings - from AI-as-a-Service to full infrastructure control, find the right option for your workloads.