24 au 26 février, 2027
Montréal, Canada
Local AI in Practice
Tired of cloud API latency and rate limits? Learn how to run, quantize, and serve open-weight models directly on your workstation using modern runtimes, optimal precision formats, and practical hardware sizing. We'll cover:
* How to choose the right quantization method for your target memory budget
* How to select and configure the right inference engine
* Practical rules of thumb for memory bandwidth, VRAM sizing, and context window
* How to choose the right quantization method for your target memory budget
* How to select and configure the right inference engine
* Practical rules of thumb for memory bandwidth, VRAM sizing, and context window
Pratik Patel
Hugging Face
Pratik Patel is an AI Data Engineer and Developer Advocate at Hugging Face. He has written 3 books on programming, Cloud and OSS. An all around software and hardware nerd with experience in the healthcare, telecom, financial services, and startup sectors. He's co-organizer of Enterprise AI Atlanta and the Atlanta Java User Group, and conference chairperson for Devnexus. He also writes about his knowledge (and adventures) in tech, is a frequent speaker at tech events, and master builder of nachos
