Benchmarking customized models on Amazon Bedrock using LLMPerf and LiteLLM

This article discusses benchmarking customized AI models on Amazon Bedrock using open-source tools LLMPerf and LiteLLM, focusing on performance evaluation and optimization of custom foundation models.

• Amazon Bedrock Custom Model Import simplifies model deployment by offering a fully managed, scalable solution
• LLMPerf and LiteLLM are used to simulate realistic load tests and benchmark model performance
• Key performance metrics include latency, throughput, time to first token, and token generation speed
• The benchmarking process helps predict production performance and estimate costs
• Example scenario uses DeepSeek-R1-Distill-Llama-8B model with specific configuration parameters

The article emphasizes the importance of performance testing even with Amazon Bedrock's simplified deployment, helping organizations optimize their AI model implementations.