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Navigating LLM Deployment: Tips, Tricks and Techniques by Meryem Arik at Qcon London
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 Meertensai 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