The article details the author's investigation into slow Python tool startup times. They used the `python -X importtime` feature to identify import bottlenecks and visualized the resulting data using Kevin Michel's `python-importtime-graph` tool, revealing a dense treemap of import times.
Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.
LocalScore is an open benchmark to evaluate local AI task performance across various hardware configurations, measuring Prompt Processing speed, Token Generation speed, Time-to-First-Token (TTFT), and a combined LocalScore.
This guide shows how to work around low performance issues when running Ubuntu 24.04 on a Raspberry Pi 5 by adjusting the EEPROM config file to override the maximum current value.
A developer recounts how Claude Code helped resolve a critical memory usage issue in an API endpoint, reducing memory usage by 99% and providing detailed solutions and evidence.
The article explores the evolution of large language model (LLM) serving, highlighting significant advancements from pre-2020 frameworks to the introduction of vLLM in 2023. It discusses the challenges of efficient memory management in LLM serving and how vLLM's PagedAttention technique revolutionizes the field by reducing memory wastage and enabling better utilization of GPU resources.
A tool to estimate the memory requirements and performance of Hugging Face models based on quantization levels.
Investigation into the effect of DDR5 speed on local LLM inference speed.
This article discusses how to improve the performance of Pandas operations by using vectorization with NumPy. It highlights alternatives to the apply() method on larger dataframes and provides examples of using NumPy's lesser-known methods like where and select to handle complex if/then/else conditions efficiently.
The article explores 11 essential tips for leveraging the full potential of the Pandas library to boost productivity and streamline workflows in handling and analyzing complex datasets. It uses a real-world dataset from Kaggle's Airbnb listings to illustrate techniques such as chunked processing and parallel execution.
