April 8, 2024, 4:19 p.m. | Roland Meertens

InfoQ - AI, ML & Data Engineering www.infoq.com

At QCon London, Meryem Arik discussed deploying Large Language Models (LLMs). While initial proofs of concept benefit from hosted solutions, scaling demands self-hosting to cut costs, enhance performance with tailored models, and meet privacy and security requirements. She emphasized understanding deployment limits, quantization for efficiency, and optimizing inference to fully use GPU resources.

By Roland Meertens

ai benefit concept costs deployment devops efficiency hosting language language models large language large language models llm llms london ml & data engineering performance privacy privacy and security qcon qcon london 2024 quantization requirements scaling security solutions tips tricks understanding

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US