Best AI tools for< Accelerate Serving >

Denvr DataWorks AI Cloud is a cloud-based AI platform that provides end-to-end AI solutions for businesses. It offers a range of features including high-performance GPUs, scalable infrastructure, ultra-efficient workflows, and cost efficiency. Denvr DataWorks is an NVIDIA Elite Partner for Compute, and its platform is used by leading AI companies to develop and deploy innovative AI solutions.

Alluxio is a data orchestration platform designed for the cloud, offering seamless access, management, and running of AI/ML workloads. Positioned between compute and storage, Alluxio provides a unified solution for enterprises to handle data and AI tasks across diverse infrastructure environments. The platform accelerates model training and serving, maximizes infrastructure ROI, and ensures seamless data access. Alluxio addresses challenges such as data silos, low performance, data engineering complexity, and high costs associated with managing different tech stacks and storage systems.

Adjust is an AI-driven platform that helps mobile app developers accelerate their app's growth through a comprehensive suite of measurement, analytics, automation, and fraud prevention tools. The platform offers unlimited measurement capabilities across various platforms, powerful analytics and reporting features, AI-driven decision-making recommendations, streamlined operations through automation, and data protection against mobile ad fraud. Adjust also provides solutions for iOS and SKAdNetwork success, CTV and OTT performance enhancement, ROI measurement, fraud prevention, and incrementality analysis. With a focus on privacy and security, Adjust empowers app developers to optimize their marketing strategies and drive tangible growth.

Tidio is an AI-powered customer service solution that helps businesses automate support, convert more leads, and increase revenue. With Lyro AI Chatbot, businesses can answer up to 70% of customer inquiries without human intervention, freeing up support agents to focus on high-value requests. Tidio also offers live chat, helpdesk, and automation features to help businesses provide excellent customer support and grow their business.

Tidio is an AI-powered customer service solution that helps businesses automate their support and sales processes. With Lyro AI Chatbot, businesses can solve up to 70% of customer problems without human intervention. Tidio also offers live chat, helpdesk, and automation features to help businesses provide excellent customer service and grow their revenue.

EarnBetter is an AI-powered platform that offers assistance in creating professional resumes, cover letters, and job search support. The platform utilizes artificial intelligence to rewrite and reformat resumes, generate tailored cover letters, provide personalized job matches, and offer interview support. Users can upload their current resume to get started and access a range of features to enhance their job search process. EarnBetter aims to streamline the job search experience by providing free, unlimited, and professional document creation services.

HubSpot is an AI-powered platform that offers CRM, marketing, sales, customer service, and content management tools. It provides a unified platform optimized by AI, with features such as marketing automation, sales pipeline development, customer support, content creation, and data organization. HubSpot caters to businesses of all sizes, from startups to large enterprises, helping them generate leads, automate processes, and improve customer retention. The platform also offers a range of integrations and solutions tailored to different business needs.

Scale AI is an AI tool that accelerates the development of AI applications for enterprise, government, and automotive sectors. It offers Scale Data Engine for generative AI, Scale GenAI Platform, and evaluation services for model developers. The platform leverages enterprise data to build sustainable AI programs and partners with leading AI models. Scale's focus on generative AI applications, data labeling, and model evaluation sets it apart in the AI industry.

Gab AI is an uncensored and unfiltered AI platform that offers a wide range of AI tools and applications. It provides users with access to various AI characters, chatbots, image generators, and creative writing prompts. The platform aims to accelerate users' creativity and knowledge by engaging them in conversations, generating content, and exploring different AI-generated outputs.

Sense Talent Engagement Platform is an AI-powered recruitment platform that offers a comprehensive suite of tools to streamline the hiring process. It provides automation workflows, database cleanup, interview scheduling, text messaging, mass texting, WhatsApp and SMS integration, mobile app support, candidate matching, AI chatbot, job matching, scheduling bot, smart FAQ, pre-screening, sourcing, live chat, instant apply, talent CRM, generative AI, voice AI, referrals, analytics, and more. The platform caters to various industries such as financial services, healthcare, logistics, manufacturing, retail, staffing, technology, and more, helping organizations attract, engage, and retain top talent efficiently.

Builder.io is an AI-powered visual development platform that accelerates digital teams by providing design-to-code solutions. With Visual Copilot, users can transform Figma designs into production-ready code quickly and efficiently. The platform offers features like AI-powered design-to-code conversion, visual editing, and enterprise CMS integration. Builder.io enables users to streamline their development process and bring ideas to production in seconds.

Katalon is a modern, comprehensive quality management platform that helps teams of any size deliver the highest quality digital experiences. It offers a range of features including test authoring, test management, test execution, reporting & analytics, and AI-powered testing. Katalon is suitable for testers of all backgrounds, providing a single platform for testing web, mobile, API, desktop, and packaged apps. With AI capabilities, Katalon simplifies test automation, streamlines testing operations, and scales testing programs for enterprise teams.

Tabnine is an AI code assistant that accelerates and simplifies software development while keeping your code private, secure, and compliant. It offers industry-leading AI code assistance, personalized to fit your team's needs, ensuring total code privacy, and providing complete protection from intellectual property issues. Tabnine's AI agents cover various aspects of the software development lifecycle, from code generation and explanations to testing, documentation, and bug fixes.

Rayyan is an intelligent systematic review tool trusted by over 500,000 researchers worldwide. It helps users organize, manage, and accelerate collaborative systematic literature reviews. Rayyan empowers users to work remotely and collaborate with distributed research teams, offering membership packages with onboarding, training, and priority support. The tool is designed to understand language, learn from user decisions, and facilitate quick navigation through systematic reviews. Rayyan also provides solutions for organizations and businesses to streamline research processes and save valuable researcher time.

Patsnap is an AI-powered innovation intelligence platform that provides data, analytics, and expertise for innovative companies. It revolutionizes how IP and R&D teams collaborate by helping them analyze the competitive landscape, reduce risks, and accelerate the entire innovation lifecycle. Patsnap offers a connected product suite trusted by over 12,000 innovators worldwide, saving valuable time and resources. The platform enables users to ideate, validate, review, submit, and monitor innovations with advanced AI tools and collaboration features.

Tricentis is an AI-powered testing tool that offers a comprehensive set of test automation capabilities to address various testing challenges. It provides end-to-end test automation solutions for a wide range of applications, including Salesforce, mobile testing, performance testing, and data integrity testing. Tricentis leverages advanced ML technologies to enable faster and smarter testing, ensuring quality at speed with reduced risk, time, and costs. The platform also offers continuous performance testing, change and data intelligence, and model-based, codeless test automation for mobile applications.

FlashIntel is a revenue acceleration platform that offers a suite of tools and solutions to streamline sales and partnership processes. It provides features like real-time enrichment, personalized messaging, sequence and cadence, email deliverability, parallel dialing, account-based marketing, and more. The platform aims to help businesses uncover ideal prospects, target key insights, craft compelling outreach sequences, research companies and people's contacts in real-time, and execute omnichannel sequences with AI personalization.

Mailmodo is an interactive email marketing platform that allows users to create, send, and automate interactive AMP emails without any development intervention. With features like Smart Segmentation, Managed Deliverability, and Automated Email Journeys, Mailmodo helps businesses boost their email conversions and engagement rates. The platform offers drag & drop editor, email API services, and 40+ integrations with favorite platforms. Mailmodo's AI tool optimizes subject lines, pre-headers, and email copy, making email creation efficient and effective. The platform ensures outstanding deliverability, personalized support, and security compliance, making it a comprehensive solution for email marketing needs.

Juro is an intelligent contract automation platform that empowers modern businesses to agree and manage contracts faster in one AI-native workspace. It offers a comprehensive suite of features such as creating contracts from browser-native templates, automating contract reminders, integrating with core platforms, and providing advanced electronic signature capabilities. Juro also enables users to extract key data from contracts, collaborate with AI-native workflows, and track obligations and risks automatically. With a focus on security and efficiency, Juro is designed to streamline the contract management process for legal, HR, procurement, sales, and finance teams across various industries.

Novita AI is an AI cloud platform that offers Model APIs, Serverless, and GPU Instance solutions integrated into one cost-effective platform. It provides tools for building AI products, scaling with serverless architecture, and deploying with GPU instances. Novita AI caters to startups and businesses looking to leverage AI technologies without the need for extensive machine learning expertise. The platform also offers a Startup Program, 24/7 service support, and has received positive feedback for its reasonable pricing and stable API services.

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

FlexFlow Serve is an open-source compiler and distributed system for **low latency**, **high performance** LLM serving. FlexFlow Serve outperforms existing systems by 1.3-2.0x for single-node, multi-GPU inference and by 1.4-2.4x for multi-node, multi-GPU inference.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.

QServe is a serving system designed for efficient and accurate Large Language Models (LLM) on GPUs with W4A8KV4 quantization. It achieves higher throughput compared to leading industry solutions, allowing users to achieve A100-level throughput on cheaper L40S GPUs. The system introduces the QoQ quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache, addressing runtime overhead challenges. QServe improves serving throughput for various LLM models by implementing compute-aware weight reordering, register-level parallelism, and fused attention memory-bound techniques.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

ServerlessLLM is a fast, affordable, and easy-to-use library designed for multi-LLM serving, optimized for environments with limited GPU resources. It supports loading various leading LLM inference libraries, achieving fast load times, and reducing model switching overhead. The library facilitates easy deployment via Ray Cluster and Kubernetes, integrates with the OpenAI Query API, and is actively maintained by contributors.

Spice is a portable runtime written in Rust that offers developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. It connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN. Built with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB. Spice makes it fast and easy to query data from one or more sources using SQL, co-locating a managed dataset with applications or machine learning models, and accelerating it with Arrow in-memory, SQLite/DuckDB, or attached PostgreSQL for fast, high-concurrency, low-latency queries.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

LLM-on-Ray is a comprehensive solution for building, customizing, and deploying Large Language Models (LLMs). It simplifies complex processes into manageable steps by leveraging the power of Ray for distributed computing. The tool supports pretraining, finetuning, and serving LLMs across various hardware setups, incorporating industry and Intel optimizations for performance. It offers modular workflows with intuitive configurations, robust fault tolerance, and scalability. Additionally, it provides an Interactive Web UI for enhanced usability, including a chatbot application for testing and refining models.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

The LLM Engineer Toolkit is a curated repository containing over 120 LLM libraries categorized for various tasks such as training, application development, inference, serving, data extraction, data generation, agents, evaluation, monitoring, prompts, structured outputs, safety, security, embedding models, and other miscellaneous tools. It includes libraries for fine-tuning LLMs, building applications powered by LLMs, serving LLM models, extracting data, generating synthetic data, creating AI agents, evaluating LLM applications, monitoring LLM performance, optimizing prompts, handling structured outputs, ensuring safety and security, embedding models, and more. The toolkit covers a wide range of tools and frameworks to streamline the development, deployment, and optimization of large language models.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

CodeTF is a Python transformer-based library for code large language models (Code LLMs) and code intelligence. It provides an interface for training and inferencing on tasks like code summarization, translation, and generation. The library offers utilities for code manipulation across various languages, including easy extraction of code attributes. Using tree-sitter as its core AST parser, CodeTF enables parsing of function names, comments, and variable names. It supports fast model serving, fine-tuning of LLMs, various code intelligence tasks, preprocessed datasets, model evaluation, pretrained and fine-tuned models, and utilities to manipulate source code. CodeTF aims to facilitate the integration of state-of-the-art Code LLMs into real-world applications, ensuring a user-friendly environment for code intelligence tasks.

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. It provides a Plain C/C++ implementation without any dependencies, optimized for Apple silicon via ARM NEON, Accelerate and Metal frameworks, and supports various architectures like AVX, AVX2, AVX512, and AMX. It offers integer quantization for faster inference, custom CUDA kernels for NVIDIA GPUs, Vulkan and SYCL backend support, and CPU+GPU hybrid inference. llama.cpp is the main playground for developing new features for the ggml library, supporting various models and providing tools and infrastructure for LLM deployment.

Awesome-LLMOps is a curated list of the best LLMOps tools, providing a comprehensive collection of frameworks and tools for building, deploying, and managing large language models (LLMs) and AI agents. The repository includes a wide range of tools for tasks such as building multimodal AI agents, fine-tuning models, orchestrating applications, evaluating models, and serving models for inference. It covers various aspects of the machine learning operations (MLOps) lifecycle, from training to deployment and observability. The tools listed in this repository cater to the needs of developers, data scientists, and machine learning engineers working with large language models and AI applications.

TPI-LLM (Tensor Parallelism Inference for Large Language Models) is a system designed to bring LLM functions to low-resource edge devices, addressing privacy concerns by enabling LLM inference on edge devices with limited resources. It leverages multiple edge devices for inference through tensor parallelism and a sliding window memory scheduler to minimize memory usage. TPI-LLM demonstrates significant improvements in TTFT and token latency compared to other models, and plans to support infinitely large models with low token latency in the future.

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