5 Brutal Truths About GPU Burn (Founders Build—Investors Watch the Bill)

Read time: 2.5 minutes

What Can You Do to Stop Your GPU From Destroying itself and You?

1. Understand What You Should Be Measuring
Performance isn't just about measuring
• Keep track of the cost per inferences
• Keep track of cost per customer

2. Optimize Your Models Before Scaling
Bigger isn't always better
• Try to use smaller models at the beginning
• Try to fine-tune a model instead of training one from the original state.

3. Monitor Real-Time Costs
There really is no “later”.
• Continue to monitor your GPU cost per experiment
• Track how much you have spent on each feature you shipped to end-users.

4. Right-Size Your Infrastructure
Nothing requires a high-end GPU to run
• Use a mixture of GPU tiers, and when possible, use a lower cost of compute to move workloads

5. Relate the Amount of Money You Spend on Your GPU to the Business Outcome That Occurs from It
If you are fast but generate no revenue, you will burn money using your GPU
• Correlate the use of your GPU to the revenue and retention produced by your device.

💡Key Takeaway: 

Not only are you growing AI, but you are also increasing your expenses. Be thoughtful in your choices.