AWS AI chips deliver high performance and low cost for Llama 3.1 models on AWS

This blog post from AWS Machine Learning discusses how AWS Trainium and AWS Inferentia chips enable high performance and low cost for fine-tuning and deploying Meta's Llama 3.1 large language models (LLMs) on AWS.

Specifically, the article covers:

• Overview of Llama 3.1 models (8B, 70B, and 405B sizes)
• Using Amazon Bedrock powered by Trainium for Llama 3.1 deployment
• Using Amazon SageMaker with Trainium support (coming soon) for Llama 3.1 fine-tuning and deployment
• Steps to fine-tune Llama 3.1 8B and 70B models on Trainium using AWS Neuron SDK
• Deploying Llama 3.1 8B and 70B models on Trainium using AWS Neuron SDK and Hugging Face
• Deploying Llama 3.1 8B using vLLM library on Trainium or Inferentia
• Conclusion on using AWS AI chips for high performance and low cost with Llama 3.1 models