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How to make LLMs go fast

This post is a long and wide-ranging survey of a bunch of different ways to make LLMs go brrrr, from better hardware utilization to clever decoding tricks.

https://vgel.me/posts/faster-inference
LLMs and Programming in the first days of 2024

http://antirez.com/news/140
Fine Tuning Python WSGI and ASGI applications for Flask, Django, and FastAPI

In this post, the focus is on examining best practices for configuring Python web servers such as Gunicorn, Uvicorn, and Hypercorn. The architecture of the components between Python code and users will be summarized, and verification methods like load testing will be discussed to ensure the configuration can withstand user traffic.

https://tonybaloney.github.io/posts/fine-tuning-wsgi-and-asgi-applications.html
semantic-router

Semantic Router is a superfast decision layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — routing our requests using semantic meaning.

https://github.com/aurelio-labs/semantic-router
Retro on Viberary

The article reflects on the author's experience with Viberary, a side project aimed at finding books based on a particular vibe. The project was created to explore machine learning side projects and the intersection of search and recommendations, serving as a production-grade complement to the author's recent deep dive into embeddings.

https://vickiboykis.com/2024/01/05/retro-on-viberary/