Best AI tools for< Serve Models For Inference >

vLLM is a fast and easy-to-use library for LLM inference and serving. It offers state-of-the-art serving throughput, efficient management of attention key and value memory, continuous batching of incoming requests, fast model execution with CUDA/HIP graph, and various decoding algorithms. The tool is flexible with seamless integration with popular HuggingFace models, high-throughput serving, tensor parallelism support, and streaming outputs. It supports NVIDIA GPUs and AMD GPUs, Prefix caching, and Multi-lora. vLLM is designed to provide fast and efficient LLM serving for everyone.

BentoML is a platform for software engineers to build, ship, and scale AI products. It provides a unified AI application framework that makes it easy to manage and version models, create service APIs, and build and run AI applications anywhere. BentoML is used by over 1000 organizations and has a global community of over 3000 members.

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.

Substratus.AI is a fully managed private LLMs platform that allows users to serve LLMs (Llama and Mistral) in their own cloud account. It enables users to keep control of their data while reducing OpenAI costs by up to 10x. With Substratus.AI, users can utilize LLMs in production in hours instead of weeks, making it a convenient and efficient solution for AI model deployment.

TitanML is a platform that provides tools and services for deploying and scaling Generative AI applications. Their flagship product, the Titan Takeoff Inference Server, helps machine learning engineers build, deploy, and run Generative AI models in secure environments. TitanML's platform is designed to make it easy for businesses to adopt and use Generative AI, without having to worry about the underlying infrastructure. With TitanML, businesses can focus on building great products and solving real business problems.

Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.

Local AI Playground is a free and open-source native app designed for AI management, verification, and inferencing. It allows users to experiment with AI offline in a private environment without the need for a GPU. The application is memory-efficient and compact, with features like CPU inferencing, model management, and digest verification. Users can start a local streaming server for AI inferencing with just two clicks. Local AI Playground aims to simplify the AI development process and provide a user-friendly platform for AI enthusiasts.

LM Studio is an AI tool designed for discovering, downloading, and running local LLMs (Large Language Models). Users can run LLMs on their laptops offline, use models through an in-app Chat UI or a local server, download compatible model files from HuggingFace repositories, and discover new LLMs. The tool ensures privacy by not collecting data or monitoring user actions, making it suitable for personal and business use. LM Studio supports various models like ggml Llama, MPT, and StarCoder on Hugging Face, with minimum hardware/software requirements specified for different platforms.

ForgeFluencer is an AI application that serves as an essential toolkit for crafting AI influencers and generating consistent and compelling content. It offers a user-friendly platform optimized for desktop and mobile, allowing users to create models, control various aspects of content generation, edit images with AI, and more. With features like Virtual Wardrobe, Pose Controller, and Photo Studio, ForgeFluencer empowers users to elevate their projects with AI-generated content effortlessly.

AiPlus is an AI tool designed to serve as a cost-efficient model gateway. It offers users a platform to access and utilize various AI models for their projects and tasks. With AiPlus, users can easily integrate AI capabilities into their applications without the need for extensive development or resources. The tool aims to streamline the process of leveraging AI technology, making it accessible to a wider audience.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

Predibase is a platform for fine-tuning and serving Large Language Models (LLMs). It provides a cost-effective and efficient way to train and deploy LLMs for a variety of tasks, including classification, information extraction, customer sentiment analysis, customer support, code generation, and named entity recognition. Predibase is built on proven open-source technology, including LoRAX, Ludwig, and Horovod.

Tecton is an AI data platform that helps build smarter AI applications by simplifying feature engineering, generating training data, serving real-time data, and enhancing AI models with context-rich prompts. It automates data pipelines, improves model accuracy, and lowers production costs, enabling faster deployment of AI models. Tecton abstracts away data complexity, provides a developer-friendly experience, and allows users to create features from any source. Trusted by top engineering teams, Tecton streamlines ML delivery processes, improves customer interactions, and automates release processes through CI/CD pipelines.

Empower is a serverless fine-tuned LLM hosting platform that offers a developer platform for fine-tuned LLMs. It provides prebuilt task-specific base models with GPT4 level response quality, enabling users to save up to 80% on LLM bills with just 5 lines of code change. Empower allows users to own their models, offers cost-effective serving with no compromise on performance, and charges on a per-token basis. The platform is designed to be user-friendly, efficient, and cost-effective for deploying and serving fine-tuned LLMs.

Dappier is a platform that enables publishers and AI developers to monetize their content by creating branded AI agents and syndicating trusted, rights-cleared data. Users can easily connect their data sources, transform content for AI interaction, and launch custom AI agents for natural search and content recommendations. The platform offers a self-serve fine-tuning platform and a marketplace for syndicating content to AI developers, enabling users to generate new revenue streams. Dappier prioritizes data security and privacy, ensuring that training data is never shared with external parties.

Humley is a Conversational AI platform that allows users to build and launch AI assistants in under an hour. The platform provides a no-code environment for creating self-serve experiences and managing AI outputs. Humley aims to revolutionize customer experiences and boost efficiencies by making Conversational AI accessible and safe for all users. With features like Knowledge Search, Build Flows, Integrate with Systems, Capture Feedback, and Multi-Channel Support, Humley Studio offers a comprehensive toolkit for creating engaging conversational experiences. The platform empowers businesses to deliver exceptional customer service, streamline access to AI models, and improve operational efficiencies.

Amazon Q in QuickSight is a generative BI assistant that makes it easy to build and consume insights. With Amazon Q, BI users can build, discover, and share actionable insights and narratives in seconds using intuitive natural language experiences. Analysts can quickly build visuals and calculations and refine visuals using natural language. Business users can self-serve data and insights using natural language. Amazon Q is built with security and privacy in mind. It can understand and respect your existing governance identities, roles, and permissions and use this information to personalize its interactions. If a user doesn't have permission to access certain data without Amazon Q, they can't access it using Amazon Q either. Amazon Q in QuickSight is designed to meet the most stringent enterprise requirements from day one—none of your data or Amazon Q inputs and outputs are used to improve underlying models of Amazon Q for anyone but you.

PromptHero is a website that provides users with prompts for Stable Diffusion, ChatGPT, and Midjourney. It also offers a community forum where users can share their work and discuss AI-related topics. The website's slogan is "The #1 website for prompt engineering." Some of the features of PromptHero include the ability to search for prompts by model, save favorites, and generate images. The website also offers a variety of resources for users, including a prompt guide and a Discord server.

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.

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

Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for popular models like FlagEmbedding, Ember, GTE, and E5. It implements features such as no model graph compilation step, Metal support for local execution on Macs, small docker images with fast boot times, token-based dynamic batching, optimized transformers code for inference using Flash Attention, Candle, and cuBLASLt, Safetensors weight loading, and production-ready features like distributed tracing with Open Telemetry and Prometheus metrics.

VILA is a family of open Vision Language Models optimized for efficient video understanding and multi-image understanding. It includes models like NVILA, LongVILA, VILA-M3, VILA-U, and VILA-1.5, each offering specific features and capabilities. The project focuses on efficiency, accuracy, and performance in various tasks related to video, image, and language understanding and generation. VILA models are designed to be deployable on diverse NVIDIA GPUs and support long-context video understanding, medical applications, and multi-modal design.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

SLMs-Survey is a comprehensive repository that includes papers and surveys on small language models. It covers topics such as technology, on-device applications, efficiency, enhancements for LLMs, and trustworthiness. The repository provides a detailed overview of existing SLMs, their architecture, enhancements, and specific applications in various domains. It also includes information on SLM deployment optimization techniques and the synergy between SLMs and LLMs.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

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.

Vespa is an open-source search and AI engine that provides a unified platform for building and deploying search and AI applications. Vespa sample applications showcase various use cases and features of Vespa, including basic search, recommendation, semantic search, image search, text ranking, e-commerce search, question answering, search-as-you-type, and ML inference serving.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

A collection of available inference solutions for Large Language Models (LLMs) including high-throughput engines, optimization libraries, deployment toolkits, and deep learning frameworks for production environments.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

TensorRT-LLM is an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM contains components to create Python and C++ runtimes that execute those TensorRT engines. It also includes a backend for integration with the NVIDIA Triton Inference Server; a production-quality system to serve LLMs. Models built with TensorRT-LLM can be executed on a wide range of configurations going from a single GPU to multiple nodes with multiple GPUs (using Tensor Parallelism and/or Pipeline Parallelism).

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.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

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