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Load-Testing LLMs Using LLMPerf
- Load testing is crucial for ensuring that Large Language Models (LLMs) can handle expected production traffic and remain performant.
- Traditional load testing tools like Locust may not provide accurate metrics for LLMs due to their unique characteristics.
- Token-based metrics, rather than requests per second, are more suitable for measuring LLM performance.
- Important LLM-specific metrics include Time to First Token and Total Output Tokens Per Second.
- LLMPerf, built on Ray, facilitates distributed load testing to simulate production-level traffic for LLMs.
- Parameters in LLMPerf like input/output token length, concurrent requests, and test duration are crucial for load testing LLMs.
- LLMPerf can be applied to Amazon Bedrock using LiteLLM API format for benchmarking different LLM models.
- Configuring LLMPerf with specific values allows for testing LLMs hosted on platforms like Bedrock with Amazon SageMaker.
- Post-testing, parsing the output files using tools like pandas provides detailed performance metrics of LLMs.
- Load testing with LLMPerf helps in selecting the right model and deployment stack for optimal LLM performance in production.
- The article focuses on the importance of load testing LLMs and provides guidance on using LLMPerf for evaluating LLM performance.
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