​AI Training: From Pytorch to GPU Clusters

​We're Homebrew, an AI Research Lab. We're the creators and lead open source maintainers of:

• ​​Jan.ai: Personal AI (>1.3mn downloads)

• ​​Cortex.so: Local AI engine

• ​​llama3-s: Llama3 + Native Speech ability

​Together with SMU AI Club - We'll share how we are training llama3-s, our fusion model with native audio comprehension

​We'll share the end-to-end details of training AI at different layers of the stack: from software pytorch level, to GPUs, to multi-clusters and data centers.

​​Event Details:

• ​​📅 12 Sept 2024, Thursday

• ​​🕒 7pm-8.30pm

• ​​📍 SMU Connexion Event Square (Level 5) (40 Stamford Rd, Singapore 178908)

• ​🍕 F&B is provided!

​​What we'll cover:

• ​​How does AI training work at a hardware level?

• ​​Training: Why did GPT-4 cost $100mn to train?

• ​​Hardware: Why did Elon Musk spend $4bn to build a 100,000 GPU cluster?

• ​​Datacenters: Why is there a shortage of AI-ready datacenters?

​​Who should attend:

• ​Anyone (non-tech included) that is interested to learn more about AI and GPUs

• ​AI Researchers and Practitioners interested in technical aspects of AI model training & optimization

• ​Students studying computer science, AI, business, economics interested to understand the infrastructure and costs associated with training AI

• ​Tech Entrepreneurs & Startup Founders who are building AI startups

• ​Investors, Industry Analysts interested to tracking trends in AI development and infrastructure

• ​Policy Makers, Responsible AI advocates who to understand the technical landscape to inform regulations and guidelines