Best AI tools for< Allocate Memory >

Mosaic is a modern, automated, and AI-powered resource planning, management, and forecasting software designed to maximize profitability by providing a fast, easy, and visual way to plan resource allocation, assemble project teams, and understand workload capacity. It offers features such as AI team building, workload forecasting, headcount planning, and capacity planning. Mosaic helps organizations improve planning efficiency, drive profitability, and reduce burnout by visualizing workload, managing people together, and building project schedules around actual capacity. The software provides real-time reports, out-of-the-box reporting, and dashboard analytics for better decision-making. Mosaic is collaborative, intuitive, and automated, making complex processes visual and easy to use.

Likely.AI is an AI-powered platform designed for the real estate industry, offering innovative solutions to enhance database management, marketing content creation, and predictive analytics. The platform utilizes advanced AI models to predict likely sellers, update contact information, and trigger automated notifications, ensuring real estate professionals stay ahead of the competition. With features like contact enrichment, predictive modeling, 24/7 contact monitoring, and AI-driven marketing content generation, Likely.AI revolutionizes how real estate businesses operate and engage with their clients. The platform aims to streamline workflows, improve lead generation, and maximize ROI for users in the residential real estate sector.

Fuel50 is an AI Talent Marketplace that offers personalized skill development paths and growth opportunities for employees. It leverages expert-driven skills ontology to align individual aspirations with organizational goals, enhance internal mobility, boost employee engagement, and increase retention. Fuel50 provides deep insights into workforce capabilities, enabling data-driven decisions for creating agile, resilient teams. The platform offers features like automated skill to role mapping, talent allocation, employee-driven career pathways, and holistic employee growth.

Connected-Stories is the next generation of Creative Management Platforms powered by AI. It is a cloud-based platform that helps creative teams to manage their projects, collaborate with each other, and track their progress. Connected-Stories uses AI to automate many of the tasks that are typically associated with creative management, such as scheduling, budgeting, and resource allocation. This allows creative teams to focus on their work and be more productive.

Epicflow is an AI-based multi-project and resource management software designed to help organizations deliver more projects on time with available resources, increase profitability, and make informed project decisions using real-time data and predictive analytics. The software bridges demand and supply by matching talent based on competencies, experience, and availability. It offers features like AI assistant, What-If Analysis, Future Load Graph, Historical Load Graph, Task List, and Competence Management Pipeline. Epicflow is trusted by leading companies in various industries for high performance and flawless project delivery.

FYLD is a fieldwork execution platform powered by AI that revolutionizes the way work is done. It provides true visibility into fieldwork operations, enabling smarter and faster decision-making that impacts productivity, safety, sustainability, and revenue for organizations worldwide.

Mudder AI is a data-powered coordination platform designed for resilient emergency response. The platform offers intelligent solutions for faster community recovery by uniting data, optimizing disaster response, and empowering emergency management through integrated intelligence. It combines data integration and harmonization, real-time situational awareness, predictive analytics and modeling, intelligent resource allocation and deployment, collaborative decision support, and post-disaster assessment and recovery. Mudder AI provides a comprehensive suite of professional services for a diverse clientele, from homeowners to commercial developers, to enhance disaster management capabilities and ensure efficient resource allocation during emergencies.

BrightBid is an AI-powered advertising optimization tool that helps users maximize the performance of their ad campaigns. By leveraging AI and automation, BrightBid enables users to make data-driven decisions, automate bids, find keywords, create ad copy, and allocate budgets easily. The tool provides intuitive dashboards for tracking ad performance, monitoring targets and metrics, controlling budget spend, and gaining competitive insights by tracking competitors. BrightBid aims to supercharge ads and boost ROI by utilizing AI technology to optimize advertising strategies and increase return on investment.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

TornadoVM is a plug-in to OpenJDK and GraalVM that allows programmers to automatically run Java programs on heterogeneous hardware. TornadoVM targets OpenCL, PTX and SPIR-V compatible devices which include multi-core CPUs, dedicated GPUs (Intel, NVIDIA, AMD), integrated GPUs (Intel HD Graphics and ARM Mali), and FPGAs (Intel and Xilinx).

HAMi is a Heterogeneous AI Computing Virtualization Middleware designed to manage Heterogeneous AI Computing Devices in a Kubernetes cluster. It allows for device sharing, device memory control, device type specification, and device UUID specification. The tool is easy to use and does not require modifying task YAML files. It includes features like hard limits on device memory, partial device allocation, streaming multiprocessor limits, and core usage specification. HAMi consists of components like a mutating webhook, scheduler extender, device plugins, and in-container virtualization techniques. It is suitable for scenarios requiring device sharing, specific device memory allocation, GPU balancing, low utilization optimization, and scenarios needing multiple small GPUs. The tool requires prerequisites like NVIDIA drivers, CUDA version, nvidia-docker, Kubernetes version, glibc version, and helm. Users can install, upgrade, and uninstall HAMi, submit tasks, and monitor cluster information. The tool's roadmap includes supporting additional AI computing devices, video codec processing, and Multi-Instance GPUs (MIG).

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

React Native binding of llama.cpp, which is an inference of LLaMA model in pure C/C++. This tool allows you to use the LLaMA model in your React Native applications for various tasks such as text completion, tokenization, detokenization, and embedding. It provides a convenient interface to interact with the LLaMA model and supports features like grammar sampling and mocking for testing purposes.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

Aphrodite is an inference engine optimized for serving HuggingFace-compatible models at scale. It leverages vLLM's Paged Attention technology to deliver high-performance model inference for multiple concurrent users. The engine supports continuous batching, efficient key/value management, optimized CUDA kernels, quantization support, distributed inference, and modern samplers. It can be easily installed and launched, with Docker support for deployment. Aphrodite requires Linux or Windows OS, Python 3.8 to 3.12, and CUDA >= 11. It is designed to utilize 90% of GPU VRAM but offers options to limit memory usage. Contributors are welcome to enhance the engine.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

ClearML Fractional GPU is a tool designed to optimize GPU resource utilization by allowing multiple containers to run on the same GPU with driver-level memory limitation and compute time-slicing. It supports CUDA 11.x & CUDA 12.x, preventing greedy processes from grabbing the entire GPU memory. The tool offers options like Dynamic GPU Slicing, Container-based Memory Limits, and Kubernetes-based Static MIG Slicing to enhance hardware utilization and workload performance for AI development.

This repository provides configuration and scripts for running Ollama LLM server on Apple Silicon Macs in headless mode, optimized for performance and resource usage. It includes features like automatic startup, system resource optimization, external network access, proper logging setup, and SSH-based remote management. Users can customize the Ollama service configuration and enable optional GPU memory optimization and Docker autostart for container applications. The installation process disables unnecessary system services, configures power management, and optimizes for background operation while maintaining Screen Sharing capability for remote management. Performance considerations focus on reducing memory usage, disabling GUI-related services, minimizing background processes, preventing sleep/hibernation, and optimizing for headless operation.

AgenticSeek is a voice-enabled AI assistant powered by DeepSeek R1 agents, offering a fully local alternative to cloud-based AI services. It allows users to interact with their filesystem, code in multiple languages, and perform various tasks autonomously. The tool is equipped with memory to remember user preferences and past conversations, and it can divide tasks among multiple agents for efficient execution. AgenticSeek prioritizes privacy by running entirely on the user's hardware without sending data to the cloud.

TotalSegmentator is a 3D Slicer extension designed for fully automatic whole body CT segmentation using the 'TotalSegmentator' AI model. The computation time is less than one minute, making it efficient for research purposes. Users can set up GPU acceleration for faster segmentation. The tool provides a user-friendly interface for loading CT images, creating segmentations, and displaying results in 3D. Troubleshooting steps are available for common issues such as failed computation, GPU errors, and inaccurate segmentations. Contributions to the extension are welcome, following 3D Slicer contribution guidelines.

Meta Lingua is a minimal and fast LLM training and inference library designed for research. It uses easy-to-modify PyTorch components to experiment with new architectures, losses, and data. The codebase enables end-to-end training, inference, and evaluation, providing tools for speed and stability analysis. The repository contains essential components in the 'lingua' folder and scripts that combine these components in the 'apps' folder. Researchers can modify the provided templates to suit their experiments easily. Meta Lingua aims to lower the barrier to entry for LLM research by offering a lightweight and focused codebase.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

AIShield Watchtower is a tool designed to fortify the security of AI/ML models and Jupyter notebooks by automating model and notebook discoveries, conducting vulnerability scans, and categorizing risks into 'low,' 'medium,' 'high,' and 'critical' levels. It supports scanning of public GitHub repositories, Hugging Face repositories, AWS S3 buckets, and local systems. The tool generates comprehensive reports, offers a user-friendly interface, and aligns with industry standards like OWASP, MITRE, and CWE. It aims to address the security blind spots surrounding Jupyter notebooks and AI models, providing organizations with a tailored approach to enhancing their security efforts.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

Optimizes project resources to ensure efficient delivery.

Coordinates project timelines ensuring efficient workflow and productivity.

Agile assistant for story points estimation.

Guides budget planning and control within projects.

Structured process for prioritizing marketing tasks based on strategic alignment. Outputs in Eisenhower, RACI and other methodologies.