Large Language Models in Production
Leveraging various deployment options
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If you’ve been experimenting with open-source models of different sizes, you’re probably asking yourself: what’s the most efficient way to deploy them?
What’s the pricing difference between on-demand and serverless providers, and is it really worth dealing with a player like AWS when there are LLM serving platforms?
I’ve decided to dive into this subject, comparing cloud vendors like AWS with newer alternatives like Modal, BentoML, Replicate, Hugging Face Endpoints, and Beam.
We’ll look at metrics such as processing time, cold start delays, and CPU, memory, and GPU costs to understand what’s most efficient and economical. We’ll also cover softer metrics like ease of deployment, developer experience and community.
