Best AI tools for< Optimize Cpu >

Rafay is an AI-powered platform that accelerates cloud-native and AI/ML initiatives for enterprises. It provides automation for Kubernetes clusters, cloud cost optimization, and AI workbenches as a service. Rafay enables platform teams to focus on innovation by automating self-service cloud infrastructure workflows.

Wallaroo.AI is an AI inference platform that offers production-grade AI inference microservices optimized on OpenVINO for cloud and Edge AI application deployments on CPUs and GPUs. It provides hassle-free AI inferencing for any model, any hardware, anywhere, with ultrafast turnkey inference microservices. The platform enables users to deploy, manage, observe, and scale AI models effortlessly, reducing deployment costs and time-to-value significantly.

Anycores is an AI tool designed to optimize the performance of deep neural networks and reduce the cost of running AI models in the cloud. It offers a platform that provides automated solutions for tuning and inference consultation, optimized networks zoo, and platform for reducing AI model cost. Anycores focuses on faster execution, reducing inference time over 10x times, and footprint reduction during model deployment. It is device agnostic, supporting Nvidia, AMD GPUs, Intel, ARM, AMD CPUs, servers, and edge devices. The tool aims to provide highly optimized, low footprint networks tailored to specific deployment scenarios.

ONNX Runtime is a production-grade AI engine designed to accelerate machine learning training and inferencing in various technology stacks. It supports multiple languages and platforms, optimizing performance for CPU, GPU, and NPU hardware. ONNX Runtime powers AI in Microsoft products and is widely used in cloud, edge, web, and mobile applications. It also enables large model training and on-device training, offering state-of-the-art models for tasks like image synthesis and text generation.

Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for speed, modularity, and expressiveness, allowing users to define models and optimization through configuration without hard-coding. Caffe supports both CPU and GPU training, making it suitable for research experiments and industry deployment. The framework is extensible, actively developed, and tracks the state-of-the-art in code and models. Caffe is widely used in academic research, startup prototypes, and large-scale industrial applications in vision, speech, and multimedia.

Pixian.AI is an AI tool that specializes in removing backgrounds from images. It offers a free service with no signup required, providing great quality results at an unbeatable price. Users can upload images, have the background removed, and download the edited image. The tool uses powerful GPUs and multi-core CPUs to analyze images efficiently. Pixian.AI also offers additional features such as creating face stickers and optimizing images. The application is designed to be user-friendly and efficient, catering to a wide range of image editing needs.

Modal is a high-performance cloud platform designed for developers, AI data, and ML teams. It offers a serverless environment for running generative AI models, large-scale batch jobs, job queues, and more. With Modal, users can bring their own code and leverage the platform's optimized container file system for fast cold boots and seamless autoscaling. The platform is engineered for large-scale workloads, allowing users to scale to hundreds of GPUs, pay only for what they use, and deploy functions to the cloud in seconds without the need for YAML or Dockerfiles. Modal also provides features for job scheduling, web endpoints, observability, and security compliance.

Deep Live Cam is a cutting-edge AI tool that enables real-time face swapping and one-click video deepfakes. It harnesses advanced AI algorithms to deliver high-quality face replacement with just a single image. The tool supports multiple execution platforms, including CPU, NVIDIA CUDA, and Apple Silicon, providing users with flexibility and optimized performance. Deep Live Cam promotes ethical use by incorporating safeguards to prevent processing of inappropriate content. Additionally, it benefits from an active open-source community, ensuring ongoing support and improvements to stay at the forefront of technology.

Jobscan is a comprehensive job search tool that helps job seekers optimize their resumes, cover letters, and LinkedIn profiles to increase their chances of getting interviews. It uses artificial intelligence and machine learning technology to analyze job descriptions and identify the skills and keywords that recruiters are looking for. Jobscan then provides personalized suggestions on how to tailor your application materials to each specific job you apply for. In addition to its resume and cover letter optimization tools, Jobscan also offers a job tracker, a LinkedIn optimization tool, and a career change tool. With its powerful suite of features, Jobscan is an essential tool for any job seeker who wants to land their dream job.

TestMarket is an AI-powered sales optimization platform for online marketplace sellers. It offers a range of services to help sellers increase their visibility, boost sales, and improve their overall performance on marketplaces such as Amazon, Etsy, and Walmart. TestMarket's services include product promotion, keyword analysis, Google Ads and SEO optimization, and advertising optimization.

VWO is a comprehensive experimentation platform that enables businesses to optimize their digital experiences and maximize conversions. With a suite of products designed for the entire optimization program, VWO empowers users to understand user behavior, validate optimization hypotheses, personalize experiences, and deliver tailored content and experiences to specific audience segments. VWO's platform is designed to be enterprise-ready and scalable, with top-notch features, strong security, easy accessibility, and excellent performance. Trusted by thousands of leading brands, VWO has helped businesses achieve impressive growth through experimentation loops that shape customer experience in a positive direction.

Botify AI is an AI-powered tool designed to assist users in optimizing their website's performance and search engine rankings. By leveraging advanced algorithms and machine learning capabilities, Botify AI provides valuable insights and recommendations to improve website visibility and drive organic traffic. Users can analyze various aspects of their website, such as content quality, site structure, and keyword optimization, to enhance overall SEO strategies. With Botify AI, users can make data-driven decisions to enhance their online presence and achieve better search engine results.

SiteSpect is an AI-driven platform that offers A/B testing, personalization, and optimization solutions for businesses. It provides capabilities such as analytics, visual editor, mobile support, and AI-driven product recommendations. SiteSpect helps businesses validate ideas, deliver personalized experiences, manage feature rollouts, and make data-driven decisions. With a focus on conversion and revenue success, SiteSpect caters to marketers, product managers, developers, network operations, retailers, and media & entertainment companies. The platform ensures faster site performance, better data accuracy, scalability, and expert support for secure and certified optimization.

EverSQL is an AI-powered SQL query optimizer and database observability tool that specializes in optimizing PostgreSQL and MySQL databases. It offers automatic SQL query optimization, ongoing performance insights, and cost reduction recommendations. With over 100,000 professionals trusting EverSQL, it aims to save time and improve database performance by making SQL queries faster and more efficient.

Rewatch is an AI-powered meeting assistant and video hub application that helps users capture meetings, create summaries, transcriptions, and action items. It centralizes all meeting videos, notes, and discussions in one place, enabling users to record themselves, their screens, or both for video messaging. Rewatch replaces repetitive in-person meetings with asynchronous collaborative series and integrates with best-in-class tools to support workflow. It aims to eliminate useless meetings, enhance strategic meetings, and power cross-functional teamwork by amplifying the voice of customers and establishing a company knowledge base. The application empowers users with conversation intelligence and actionable insights, making communication and collaboration effortless in a unified hub.

Competera is an AI-powered pricing platform designed for online and omnichannel retailers. It offers a unified workplace with an easy-to-use interface, real-time market data, and AI-powered product matching. Competera focuses on demand-based pricing, customer-centric pricing, and balancing price elasticity with competitive pricing. It provides granular pricing at the SKU level and offers a seamless adoption and onboarding process. The platform helps retailers optimize pricing strategies, increase margins, and save time on repricing.

Inventoro is a smart inventory forecasting and replenishment tool that helps businesses optimize their inventory management processes. By analyzing past sales data, the tool predicts future sales, recommends order quantities, reduces inventory size, identifies profitable inventory items, and ensures customer satisfaction by avoiding stockouts. Inventoro offers features such as sales forecasting, product segmentation, replenishment, system integration, and forecast automations. The tool is designed to help businesses decrease inventory, increase revenue, save time, and improve product availability. It is suitable for businesses of all sizes and industries looking to streamline their inventory management operations.

Vic.ai is an AI-powered accounting software designed to streamline invoice processing, purchase order matching, approval flows, payments, analytics, and insights. The platform offers autonomous finance solutions that optimize accounts payable processes, achieve lasting ROI, and enable informed decision-making. Vic.ai leverages AI technology to enhance productivity, accuracy, and efficiency in accounting workflows, reducing manual tasks and improving overall financial operations.

TimeToTok is an AI Copilot and Agent designed for TikTok creators to optimize their growth on the platform. It utilizes LLM AI technology to analyze massive TikTok data and provide personalized insights and actions for improving content performance and engagement. With features like identifying the best time to post, generating viral ideas, optimizing videos, tracking competitors, and providing growth suggestions, TimeToTok aims to help creators achieve significant growth and success on TikTok.

Paro is a professional business finance and accounting solutions platform that matches businesses and accounting firms with skilled finance experts. It offers a wide range of services including accounting, bookkeeping, financial planning, budgeting, business analysis, data visualization, strategic advisory, growth strategy consulting, startup and fundraising consulting, transaction advisory, tax and compliance services, AI consulting services, and more. Paro aims to help businesses optimize faster by providing expert solutions to bridge gaps in finance and accounting operations. The platform also offers staff augmentation services, talent acquisition, and custom solutions to enhance operational efficiency and maximize ROI.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

Node.js bindings for OpenAI's Whisper model that automatically converts audio to WAV format with a 16000 Hz frequency to support the whisper model. It outputs transcripts to various formats, is optimized for CPU including Apple Silicon ARM, provides timestamp precision to single word, allows splitting on word rather than token, translation from source language to English, and conversion of audio format to WAV for whisper model support.

GLake is an acceleration library and utilities designed to optimize GPU memory management and IO transmission for AI large model training and inference. It addresses challenges such as GPU memory bottleneck and IO transmission bottleneck by providing efficient memory pooling, sharing, and tiering, as well as multi-path acceleration for CPU-GPU transmission. GLake is easy to use, open for extension, and focuses on improving training throughput, saving inference memory, and accelerating IO transmission. It offers features like memory fragmentation reduction, memory deduplication, and built-in security mechanisms for troubleshooting GPU memory issues.

PowerInfer is a high-speed Large Language Model (LLM) inference engine designed for local deployment on consumer-grade hardware, leveraging activation locality to optimize efficiency. It features a locality-centric design, hybrid CPU/GPU utilization, easy integration with popular ReLU-sparse models, and support for various platforms. PowerInfer achieves high speed with lower resource demands and is flexible for easy deployment and compatibility with existing models like Falcon-40B, Llama2 family, ProSparse Llama2 family, and Bamboo-7B.

Prime is a framework for efficient, globally distributed training of AI models over the internet. It includes features such as fault-tolerant training with ElasticDeviceMesh, asynchronous distributed checkpointing, live checkpoint recovery, custom Int8 All-Reduce Kernel, maximizing bandwidth utilization, PyTorch FSDP2/DTensor ZeRO-3 implementation, and CPU off-loading. The framework aims to optimize communication, checkpointing, and bandwidth utilization for large-scale AI model training.

Scalene is a high-performance CPU, GPU, and memory profiler for Python that provides detailed information and runs faster than many other profilers. It incorporates AI-powered proposed optimizations, allowing users to generate optimization suggestions by clicking on specific lines or regions of code. Scalene separates time spent in Python from native code, highlights hotspots, and identifies memory usage per line. It supports GPU profiling on NVIDIA-based systems and detects memory leaks. Users can generate reduced profiles, profile specific functions using decorators, and suspend/resume profiling for background processes. Scalene is available as a pip or conda package and works on various platforms. It offers features like profiling at the line level, memory trends, copy volume reporting, and leak detection.

This GitHub repository contains examples of fine-tuning open source large language models. It showcases the process of fine-tuning and quantizing large language models using efficient techniques like Lora and QLora. The repository serves as a practical guide for individuals looking to optimize the performance of language models through fine-tuning.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

cake is a pure Rust implementation of the llama3 LLM distributed inference based on Candle. The project aims to enable running large models on consumer hardware clusters of iOS, macOS, Linux, and Windows devices by sharding transformer blocks. It allows running inferences on models that wouldn't fit in a single device's GPU memory by batching contiguous transformer blocks on the same worker to minimize latency. The tool provides a way to optimize memory and disk space by splitting the model into smaller bundles for workers, ensuring they only have the necessary data. cake supports various OS, architectures, and accelerations, with different statuses for each configuration.

AIFoundation focuses on AI Foundation, large model systems. Large models optimize the performance of full-stack hardware and software based on AI clusters. The training process requires distributed parallelism, cluster communication algorithms, and continuous evolution in the field of large models such as intelligent agents. The course covers modules like AI chip principles, communication & storage, AI clusters, computing architecture, communication architecture, large model algorithms, training, inference, and analysis of hot technologies in the large model field.

cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.

PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.

SuperBench is a validation and profiling tool for AI infrastructure. It provides a comprehensive set of tests and benchmarks to evaluate the performance and reliability of AI systems. The tool helps users identify bottlenecks, optimize configurations, and ensure the stability of their AI infrastructure. SuperBench is designed to streamline the validation process and improve the overall efficiency of AI deployments.

PyTorch Forecasting is a PyTorch-based package for time series forecasting with state-of-the-art network architectures. It offers a high-level API for training networks on pandas data frames and utilizes PyTorch Lightning for scalable training on GPUs and CPUs. The package aims to simplify time series forecasting with neural networks by providing a flexible API for professionals and default settings for beginners. It includes a timeseries dataset class, base model class, multiple neural network architectures, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. PyTorch Forecasting is built on pytorch-lightning for easy training on various hardware configurations.

IPEX-LLM is a PyTorch library for running Large Language Models (LLMs) on Intel CPUs and GPUs with very low latency. It provides seamless integration with various LLM frameworks and tools, including llama.cpp, ollama, Text-Generation-WebUI, HuggingFace transformers, and more. IPEX-LLM has been optimized and verified on over 50 LLM models, including LLaMA, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, and RWKV. It supports a range of low-bit inference formats, including INT4, FP8, FP4, INT8, INT2, FP16, and BF16, as well as finetuning capabilities for LoRA, QLoRA, DPO, QA-LoRA, and ReLoRA. IPEX-LLM is actively maintained and updated with new features and optimizations, making it a valuable tool for researchers, developers, and anyone interested in exploring and utilizing LLMs.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

AI for GPU, CPU, memory, and storage recommendation

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