Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart

This article discusses how to benchmark and optimize the deployment of large language models (LLMs) on Amazon SageMaker JumpStart, focusing on latency, throughput, and cost optimization.

Specifically, the article covers:

• Deployed endpoint benchmarking for various LLMs (Llama 2, Falcon, Mistral) across different instance types
• How accelerator specifications impact LLM benchmarking
• Selecting an endpoint deployment configuration to minimize latency, maximize throughput, or minimize cost
• Trade-offs between tensor parallelism and multi-model deployments on a single instance
• Horizontal scaling with multiple instances behind an endpoint
• Invoking an endpoint with concurrent requests
• Conclusion and recommendations