Search Results for "cpu memory usage" - Page 2
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220 projects for "cpu memory usage" with 1 filter applied:
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R-KV
Redundancy-aware KV Cache Compression for Reasoning Models
R-KV is an open-source research project that focuses on improving the efficiency of large language model inference through key-value cache compression techniques. Modern transformer models rely heavily on KV caches during autoregressive decoding, which store intermediate attention states to accelerate generation. However, these caches can consume large amounts of memory, especially in reasoning-oriented models with long context windows. R-KV introduces a method for compressing the KV cache... -
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VulkanSceneGraph
Vulkan & C++17 based Scene Graph Project
VulkanSceneGraph (VSG), is a modern, cross-platform, high-performance scene graph library built upon Vulkan graphics/compute API. The software is written in C++17 and follows the CppCoreGuidelines and FOSS Best Practices. The source code is published under the MIT License, with the exception of vulkan.h, used for Vulkan extensions, which is under Apache License 2.0. This repository contains C++ headers and source and CMake build scripts to build the libvsg library. Additional support... -
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DeepEP
DeepEP: an efficient expert-parallel communication library
...Because MoE architectures require routing inputs to different experts, communication overhead can become a bottleneck — DeepEP addresses that by providing optimized GPU kernels and efficient dispatch/combining logic. The library also supports low-precision operations (such as FP8) to reduce memory and bandwidth usage during communication. DeepEP is aimed at large-scale model inference or training systems where expert parallelism is used to scale model capacity without replicating entire networks. -
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
AI Agent Deep Dive is a comprehensive educational repository designed to provide a deep and structured understanding of how modern AI agents work, focusing on architecture, workflows, and real-world implementation patterns. It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. The project is organized as a learning resource rather than a standalone framework, making it particularly useful for developers who want to move beyond surface-level prompt engineering into full agent system design. It explores how agents interact with environments, execute tasks, and maintain context over time, highlighting both strengths and limitations of current approaches. ... -
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php-file-iterator
FilterIterator implementation that filters files
...Exclusion lists help you skip vendor directories, VCS folders, or generated artifacts that would otherwise slow down tools. The iterator interface means results are streamed rather than loaded at once, which keeps memory usage modest on large codebases. It also plays nicely with glob patterns and SPL iterators, so you can combine it with other filesystem utilities. As part of the PHPUnit tooling suite, it’s optimized for speed and predictable behavior across platforms. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. ... -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. ... -
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DebugSwift
A toolkit to make debugging iOS applications easier
...Designed to operate within the iOS environment (iOS 14+ and Swift 6+), it includes network and WebSocket inspectors for HTTP requests, detailed performance metrics (CPU, memory, FPS), and mechanisms to detect memory leaks and main thread violations in real time. The toolkit also provides developers with crash analysis features, console log monitoring, device and build information displays, and customizable debugging panels, all of which make it easier to troubleshoot edge cases and unexpected behavior. ... -
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VisualVM
VisualVM is an All-in-One Java Troubleshooting Tool
VisualVM is a visual tool integrating command line JDK tools and lightweight profiling capabilities. Designed for both development and production time use. Use Apache Ant 1.9.9 or above and Oracle JDK 8 to build VisualVM from this repository. VisualVM monitors and troubleshoots applications running on Java 1.4+ from many vendors using various technologies including jvmstat, JMX, Serviceability Agent (SA) and Attach API. VisualVM perfectly fits all the requirements of application developers,... -
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JetCache
JetCache is a Java cache framework
JetCache is a Java cache abstraction that provides uniform usage for different caching solutions. It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
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Perfetto
Production-grade client-side tracing, profiling, and analysis
Perfetto is a production-grade tracing platform for Android, Linux, and Chrome that captures extremely detailed information about what a system is doing over time. It’s designed around a low-overhead producer/consumer model: instrumented components (“producers”) write binary events into shared memory buffers and a collector (“service”) reliably streams them to storage. The data model spans kernel and userspace, so you can stitch together CPU scheduling, app lifecycles, binder/IPC hops, GPU work, power and thermal signals, file I/O, heap samples, and more into a single coherent timeline. Perfetto’s ecosystem includes a web-based UI that can load multi-GB traces directly in the browser and an offline “trace processor” that exposes the trace as a queryable SQL-like table schema for deep analysis and automation. ... -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
mistral-finetune is an official lightweight codebase designed for memory-efficient and performant finetuning of Mistral’s open models (e.g. 7B, instruct variants). It builds on techniques like LoRA (Low-Rank Adaptation) to allow customizing models without full parameter updates, which reduces GPU memory footprint and training cost. The repo includes utilities for data preprocessing (e.g. reformat_data.py), validation scripts, and example YAML configs for training variants like 7B base or... -
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NVIDIA Merlin
Library providing end-to-end GPU-accelerated recommender systems
...Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders. Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory. Deploy data transformations and trained models to production with only a few lines of code. -
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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... -
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claude-code-best-practice
Practice made claude perfect
...By combining conceptual guidance with concrete examples and configuration patterns, the project helps teams move from experimental AI usage toward more production-ready agent orchestration. -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. ... -
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waifu2x ncnn Vulkan
waifu2x converter ncnn version, run fast GPU with vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework. -
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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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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
...The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language... -
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lru-cache
A fast cache that automatically deletes the least recently used items
node-lru-cache is a high performance in memory caching library for Node.js that automatically evicts the least recently used items when the cache reaches its size limit. It is designed to provide predictable memory usage while maintaining fast lookup and update operations. The library is widely used in server side JavaScript applications where efficient caching can significantly improve performance. -
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Honggfuzz
Security oriented software fuzzer
...The tool integrates tightly with sanitizers and can attach to already running processes, making it convenient for both white-box and black-box fuzzing. When it finds a crash, honggfuzz captures detailed context, minimizes the input, and can pin reproducibility by controlling CPU affinity and resource limits. Its small footprint and straightforward CLI make it easy to drop into CI or long-running campaigns across many cores. -
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retro-go
Retro emulation for the ODROID-GO and other ESP32 devices
...Its design makes it ideal for DIY handheld consoles and hobbyist hardware projects, where minimal resource usage is critical. -
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quicktemplate
Fast, powerful, yet easy to use template engine for Go
quicktemplate is a fast and efficient templating engine for Go that generates code at compile time to render templates with minimal runtime overhead. Unlike traditional templating systems that interpret templates dynamically, Quicktemplate compiles templates into Go code, significantly improving performance. This approach eliminates the need for reflection and reduces memory allocations, making it suitable for high-performance applications. The library is designed to be simple and... -
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TechCPP
C++ learning and interview guide aimed at back-end systems developers
...The repository organizes topics so you can quickly locate weak spots before interviews or brush up for day-to-day coding. It’s equally useful as a revision handbook and as a roadmap for engineers moving from “effective C++ usage” toward “systems-level competence”.
