In this video, Haowen Huang, Senior Developer Advocate at AWS, presents advanced strategies for integrating generative AI into your applications using large language models (LLMs) on AWS. Learn about the importance of LLM optimization, challenges in deploying memory-intensive models like Llama-3 70B, and essential model optimization techniques.
Discover how AWS simplifies these complex processes through Amazon SageMaker JumpStart, Amazon Bedrock, and Amazon SageMaker with Large model inference (LMI) container.
Whether you're an experienced AI developer or a beginner, you'll get valuable insights to streamline your LLM deployment and enhance performance!
Resources:
🔗 Meta Llama 3 models are now available in Amazon SageMaker JumpStart
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☁️ Meta Llama in Amazon Bedrock
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🛠️ Fine-tune Llama 2 models on Amazon SageMaker
[ Ссылка ]
⚡️ Boost inference performance for LLMs with new Amazon SageMaker containers
[ Ссылка ]
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Chapters:
00:53 – Understanding the importance and benefits of optimizing LLM
01:21 – Challenges in LLM optimization
03:12 – Challenges in LLM optimization (example: hosting Llama3 70B model)
03:52 – Introduction to model quantization
06:01 – Overview of LLM optimization techniques
07:00 – Introduction to AWS solutions for simplifying LLM optimization
07:24 – Deploying LLMs through Amazon SageMaker JumpStart
08:41 – Code comparation: with vs. without SageMaker JumpStart
09:50 – Deploying LLMs through Amazon Bedrock
11:45 – Discussing throughout optimization & usability (Large model inference container)
11:56 – Conclusion
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