Exploring Energy Consumption of Generative AI Models vs. Serving Static Web Content

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The chapter compares the energy consumption of generative large language models with serving static web content, highlighting the difference in resources required for generating AI responses each time. It explores strategies to reduce the energy footprint of generative AI, including training models, utilizing AI models, and running models on devices.

Scott and Wes tackle a variety of audience questions, from the nuances of over-engineering to the energy consumption of AI LLMs. They also discuss the pros and cons of monorepos, frameworks, and the ever-important question: Do you really need to learn all the developer tooling?

Show Notes

00:00 Welcome to Syntax!
00:41 Brought to you by Sentry.io.
01:07 Challenges around a resume playback function.
05:56 Why use Google Forms for Potluck questions?
07:57 What constitutes over-engineering and how to avoid it.
13:28 Webview vs native component based mobile apps.
18:06 Running and managing monorepos.
20:59 Energy consumption of AI LLMs vs static web content.
- A guide to LLM inference and performance.
- From Words to Watts: Benchmarking the Energy Costs of LLM Inference.
25:19 Why do we need frameworks?
- Frank M Taylor Blog Post.
33:05 Handling ad-blockers blocking Sentry and other tools.
- Syntax GitHub.
38:25 Creating sites without JavaScript.
42:49 Do I really have to learn all the various developer tooling?
- Wes Bos Tweet.
44:47 What are the best ways to network and meet other developers?
50:16 Sick Picks & Shameless Plugs.