Search Results for "memory usage"

whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples....

New LinuxQ83 kopernik 13.14 ,is a modern, lightweight Linux distribution focused on performance, usability, and innovation. It features the COSMIC Desktop, a next-generation environment written in Rust, offering high performance, stability, and memory safety. COSMIC provides a clean and customizable interface for efficient window and workspace management. The system is suitable for both older and newer hardware thanks to its low resource usage. LinuxQ83 also uses Nushell as its default shell, also written in Rust, enabling modern, structured data handling. Additionally, it can support Cinnamon, a traditional and user-friendly desktop environment similar to classic Windows layouts.

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...Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. A WeightsProvider specialization can implement any type of loading, caching, and prefetching of the model parameters.

...It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching, and two SLAM solutions (an Online SLAM and a Particle Filter SLAM). The main feature is efficiency. Low computational effort and low memory usage whenever possible. The minimum viable computer to run our localization and SLAM solutions is a Raspberry Pi 3 Model B+. We provide a fast scan-matching approach to mobile robot localization supported by a continuous likelihood field. It can be used to provide accurate localization for robots equipped with a laser and a not-so-good odometry. ...

Change log: 07-16-2014 – After today’s changes RAM usage on first boot now down to <100mb. 07-16-2014 – Set most logs to write memory instead of SD card. (up to 10M or 1 day max) 07-16-2014 – Reduced system log level from “info” to “warning”. Reduce SD contention. 07-16-2014 – Uninstalled ntpd daemon. Replaced with systemd-timesyncd. 07-16-2014 – Enabled PHP’s mysql, mysqli and pdo extentions 07-16-2014 – Disabled innodb. 07-15-2014 – Switched PHP-FPM to “ondemand” to save memory. 07-15-2014 – Enabled gzip. ...