Search Results for "ubuntu memory benchmark"
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Showing 125 open source projects for "ubuntu memory benchmark"
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youki
A container runtime written in Rust
...However, the container runtime requires the use of system calls, which requires a bit of special handling when implemented in Go. This is too tricky (e.g. namespaces(7), fork(2)); with Rust, it's not that tricky. And, unlike in C, Rust provides the benefit of memory safety. While Rust is not yet a major player in the container field, it has the potential to contribute a lot: something this project attempts to exemplify. youki has the potential to be faster and use less memory than runc, and therefore works in environments with tight memory usage requirements. Here is a simple benchmark of a container from creation to deletion. -
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Operit AI
Powerful Android AI agent with tools, automation, and Linux shell
...It integrates deep system-level capabilities with a wide range of tools, allowing the AI to perform real tasks such as file management, automation, and system control directly on the device. A standout aspect of the project is its built-in Ubuntu 24 environment, which enables users to run Linux commands, scripts, and development tools in a mobile context. Operit supports both local and remote AI models, including offline execution through frameworks like llama.cpp and MNN, helping preserve user privacy while maintaining flexibility. Operit also includes an intelligent memory system that stores, organizes, and retrieves user interactions to provide more personalized and context-aware responses. ... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
...Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. ... -
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Napkin Math
Techniques and numbers for estimating system's performance
Napkin Math is a technical reference project for estimating software system performance from first principles. It collects practical numbers, benchmark-style measurements, and mental models that help engineers make fast back-of-the-envelope calculations. The project is useful for questions like how much memory throughput matters, how long storage operations may take, what network latency to expect, or how expensive logging could become at high request volume. It treats these values as rounded numbers for reasoning rather than exact performance guarantees. ... -
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MemU
MemU is an open-source memory framework for AI companions
MemU is an agentic memory layer for LLM applications, specifically designed for AI companions. Transform your memory into an intelligent file system that automatically organizes, connects, and evolves with your memories. Simple, fast, and reliable memory infrastructure for AI applications. Powerful tools and dedicated support to scale your AI applications with confidence. Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs. SSO/RBAC... -
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JMH Gradle Plugin
Integrates the JMH benchmarking framework with Gradle
...The plugin is especially useful in projects where regression in execution speed or memory use must be carefully monitored. -
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Meta Agents Research Environments (ARE)
Meta Agents Research Environments is a comprehensive platform
...Unlike static benchmarks, ARE supports environments where agents must adapt to changes over time and reason over sequences of actions. It interacts with applications and faces uncertainty. The included Gaia2 benchmark offers 800 scenarios across multiple “universes”. It can test reasoning, memory, tool use, and adaptability. Integration with simulated applications/agent APIs (email, file system, etc.). Support for multiple AI model backends/providers. -
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plow
A high-performance HTTP benchmarking tool
Plow is an HTTP(S) benchmarking tool, written in Golang. It uses excellent fast HTTP instead of Go's default net/http due to its lightning-fast performance. Plow runs at specified connections (option -c) concurrently and real-time records summary statistics, histogram of execution time and calculates percentiles to display on Web UI and terminal. It can run for a set duration( option -d), for a fixed number of requests(option -n), or until Ctrl-C is interrupted. The implementation of... -
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Likwid
Performance monitoring and benchmarking suite
Likwid is a simple to install and use toolsuite of command line applications and a library for performance oriented programmers. It works for Intel, AMD, ARMv8 and POWER9 processors on the Linux operating system. There is additional support for Nvidia and AMD GPUs. There is support for ARMv7 and POWER8/9 but there is currently no test machine in our hands to test them properly. -
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
...The repository is often used as a benchmark dataset and example for teaching, benchmarking, and demonstration purposes in the data science and urban analytics communities. -
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HUSTOJ
Popular Open Source Online Judge based on PHP/C++/MySQL/Linux
Popular Open Source Online Judge based on PHP/C++/MySQL/Linux for ACM/ICPC and NOIP training, with easy installation. Open source OJ system. HUSTOJ is free software under the GPL. (Only the original part of the code, which uses components from other open-source projects, please follow the agreement of the original component.) Because the web terminal/database/judgment machine are all packaged in the same image and cannot be extended, it is not recommended to use this image for distributed... -
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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time... -
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Fiber
Express inspired web framework written in Go
...Since Fiber is built on top of Fasthttp, your apps will enjoy unmatching performance! Don't believe us? Here's a benchmark that proves how Fiber shines compared to other frameworks. Are you building an API server? We've got you covered! Fiber is the perfect choice for building REST APIs in Go. Receiving and sending data is fast and easy! -
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Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
In terminal graphical metrics for your *nix system written in Rust. The make file provides for building fully static versions on Linux against the musl C library. It requires musl-gcc to be installed on the system. Install "musl-tools" package on debian/ubuntu derivatives, "musl-gcc" on fedora and equivalent on other distributions from their standard repos. If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is... -
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Novabench
Benchmark CPU, GPU, memory, and storage
Novabench is a computer benchmarking software that helps users evaluate and compare the performance of their system’s CPU, GPU, memory, and storage. It offers rapid testing, enabling comparisons across millions of devices and providing insights for troubleshooting, upgrades, and performance optimization. -
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BitShares Core
BitShares Blockchain implementation and command-line interface
BitShares Core is the BitShares blockchain implementation and command-line interface. The web browser-based wallet is BitShares UI. BitShares requires a 64-bit operating system to build, and will not build on a 32-bit OS. BitShares requires Boost libraries to build, and supports versions 1.58 to 1.74. Newer versions may work but have not been tested. If your system came pre-installed with a version of Boost libraries that you do not wish to use, you may manually build your preferred version... -
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Ante
Ghost in your shell. Ante is a self-contained agent harness
...Ante also supports more than a dozen hosted providers and can switch between commercial, open-weight, and local models. Multi-agent orchestration lets it spawn and coordinate specialized subagents for larger software tasks. Skills, MCP integrations, persistent memory, resumable sessions, and public benchmark evaluation extend it into a lightweight general-purpose agent harness. -
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Agentex
Open source codebase for Scale Agentex
AgentEX is an open framework from Scale for building, running, and evaluating agentic workflows, with an emphasis on reproducibility and measurable outcomes rather than ad-hoc demos. It treats an “agent” as a composition of a policy (the LLM), tools, memory, and an execution runtime so you can test the whole loop, not just prompting. The repo focuses on structured experiments: standardized tasks, canonical tool interfaces, and logs that make it possible to compare models, prompts, and tool... -
20
whichllm
Find the local LLM that actually runs and performs best
whichllm is a command-line tool for finding local large language models that can realistically run on a user’s hardware. It detects the machine’s available resources, including GPU, CPU, memory, and storage, then recommends models based on practical fit rather than parameter count alone. The project is useful for users who are unsure which local LLM will perform well on their system. It focuses on real, recency-aware benchmarks so recommendations better reflect current model performance.... -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely... -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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Qwen2.5-Omni
Capable of understanding text, audio, vision, video
Qwen2.5-Omni is an end-to-end multimodal flagship model in the Qwen series by Alibaba Cloud, designed to process multiple modalities (text, images, audio, video) and generate responses both as text and natural speech in streaming real-time. It supports “Thinker-Talker” architecture, and introduces innovations for aligning modalities over time (for example synchronizing video/audio), robust speech generation, and low-VRAM/quantized versions to make usage more accessible. It holds... -
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Tile38
Ultra Fast Geospatial Database & Geofencing Server
When you need the best performance for your location-based applications, you can rely on Tile38. Tile38 is an ultra-fast, open source geospatial database and geofencing server capable of real-time geofencing, fast spatial indexing and more. It supports a variety of object types including lat/lon, Geohash, bbox, GeoJSON, QuadKey, and XYZ tile; and is capable of operations like Nearby, Within, and Intersects. There’s also built-in support for many popular tools. Tile38 is made up of 3 main... -
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Smile
Statistical machine intelligence and learning engine
Smile is a fast and comprehensive machine learning engine. With advanced data structures and algorithms, Smile delivers the state-of-art performance. Compared to this third-party benchmark, Smile outperforms R, Python, Spark, H2O, xgboost significantly. Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM languages. Data scientists and developers can speak the same language now! ...
