This is the second post in the LLM Benchmarking series, which shows how to use GenAI-Perf to benchmark the Meta Llama 3 model when deployed with NVIDIA NIM. When building LLM-based applications, it is critical to understand the performance characteristics of these models on a given hardware. This serves multiple purposes: As a client-side LLM-focused benchmarking tool…
]]>This is the first post in the large language model latency-throughput benchmarking series, which aims to instruct developers on common metrics used for LLM benchmarking, fundamental concepts, and how to benchmark your LLM applications. The past few years have witnessed the rise in popularity of generative AI and large language models (LLMs), as part of a broad AI revolution.
]]>NVIDIA offers tools like Perf Analyzer and Model Analyzer to assist machine learning engineers with measuring and balancing the trade-off between latency and throughput, crucial for optimizing ML inference performance. Model Analyzer has been embraced by leading organizations such as Snap to identify optimal configurations that enhance throughput and reduce deployment costs. However…
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