Deploying Meta Llama 3 Models on AWS Trainium and AWS Inferentia with SageMaker JumpStart
Are you looking to deploy large generative text models on AWS in a cost-effective manner? Well, we have some exciting news for you! Meta Llama 3 inference is now available on AWS Trainium and AWS Inferentia based instances in Amazon SageMaker JumpStart.
The Meta Llama 3 models are a collection of pre-trained and fine-tuned generative text models that offer developers easier access to high-performance accelerators for real-time applications such as chatbots and AI assistants. The AWS Trainium and AWS Inferentia based instances provide up to 50% lower cost to deploy these models compared to other Amazon EC2 instances.
In this blog post, we will show you how easy it is to deploy Meta Llama 3 on AWS Trainium and AWS Inferentia based instances in SageMaker JumpStart.
Meta Llama 3 model on SageMaker Studio
SageMaker JumpStart provides access to a variety of foundation models, including the Meta Llama 3 models. You can access these models through the Amazon SageMaker Studio console and the SageMaker Python SDK. SageMaker Studio offers a web-based visual interface where you can access tools for all machine learning development steps.
To find the Meta Llama 3 models in SageMaker JumpStart, simply search for “Meta” in the search box on the landing page. You can also find relevant model variants by searching for “neuron” as well.
No-code deployment of the Llama 3 Neuron model on SageMaker JumpStart
Deploying the Meta Llama 3 model is made simple through the SageMaker JumpStart SDK. You can choose the model card to view details about the model, including the license and data used to train it. Simply choose the Deploy button to deploy the model or open the example notebook for step-by-step guidance.
Meta Llama 3 deployment on AWS Trainium and AWS Inferentia using the SageMaker JumpStart SDK
You can deploy the Meta Llama 3 models on AWS Trainium and AWS Inferentia based instances using the SageMaker JumpStart SDK. The SDK provides pre-compiled models for various configurations to avoid runtime compilation during deployment and fine-tuning.
There are two ways to deploy the models using the SDK – a simple deployment with two lines of code or a more customized deployment where you can specify configurations such as sequence length, tensor parallel degree, and maximum rolling batch size.
Conclusion
The deployment of Meta Llama 3 models on AWS Inferentia and AWS Trainium using SageMaker JumpStart offers the lowest cost for deploying large-scale generative AI models like Llama 3 on AWS. These models provide flexibility, ease of use, and up to 50% lower cost compared to EC2 instances.
We hope this blog post has provided you with valuable insights on deploying Meta Llama 3 models on AWS. To get started with SageMaker JumpStart, check out the resources mentioned in the post. We are excited to see the innovative applications you will build using these models!
And that’s a wrap for today’s blog post. Stay tuned for more updates and tutorials on deploying AI models on AWS. Happy coding!