核心概念
Achieving maximum CPU performance for executing Small Language Models (SLMs) on personal computers through proper configuration and benchmarking.
要約
The article discusses the performance of Small Language Models (SLMs) when executed solely on CPU cores, without the assistance of GPU or NPU accelerators. It compares the performance of four popular SLMs running on two different systems: an AMD Ryzen 7840U and an Intel Core Ultra 7 165H.
The key insights are:
The llama.cpp project, which is the backend used by the LM Studio application, recommends setting the number of threads to the number of physical CPU cores for optimal performance. In contrast, the default thread count in LM Studio is 4, which can limit the available memory bandwidth and lead to suboptimal performance.
When the thread count is properly configured in llama.cpp, the Intel Core Ultra 7 165H outperforms the AMD Ryzen 7840U in 3 out of the 4 SLMs tested.
The article provides a step-by-step guide on how to replicate the performance testing, including the required system configuration, software setup, and command-line flags for llama.cpp.
The article emphasizes the importance of understanding the software stack and dependencies for accurate performance analysis of AI-powered applications, such as SLMs, on personal computers.
統計
The token rate, which is roughly equivalent to the number of words per second the SLM generates, is used as the performance metric.
引用
"Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance."