Serverless GPU vs Dedicated Instances
The rise of AI has created a new challenge: how do you efficiently access GPU compute for your workloads? This course explores two fundamentally different approaches - serverless GPU and dedicated instances.
The GPU Access Challenge
GPUs are expensive and powerful. Using them efficiently requires matching your access pattern to your workload:
Workload A: 1 million requests/month, 50ms each
= 13.9 GPU-hours of actual compute
Workload B: Train model continuously for 2 weeks
= 336 GPU-hours of actual compute
These workloads need very different GPU access strategies.
What You'll Learn
By the end of this course, you'll understand:
- What serverless GPU means and how it works
- When dedicated GPU instances make sense
- How to calculate costs for each approach
- A decision framework for choosing the right option
- Float16's options for both patterns
Course Structure
Chapter 1: Introduction
Overview of serverless vs dedicated computing paradigms.
Chapter 2: Serverless GPU Explained
Pay-per-request GPU access with auto-scaling.
Chapter 3: Dedicated GPU Explained
Reserved GPU instances with full control.
Chapter 4: When to Use Which
Decision framework and cost analysis.
Chapter 5: Float16 Options
Our serverless and dedicated GPU offerings.
Prerequisites
- Basic understanding of cloud computing
- Familiarity with AI/ML workloads
- No GPU experience required
Who Should Take This Course?
- ML Engineers choosing deployment strategies
- Engineering managers planning GPU budgets
- Architects designing AI infrastructure
- Anyone exploring GPU cloud options
Let's begin by understanding the core concepts of serverless and dedicated computing.