Best AI tools for< Scale Inference >

d-Matrix is an AI tool that offers ultra-low latency batched inference for generative AI technology. It introduces Corsair™, the world's most efficient AI inference platform for datacenters, providing high performance, efficiency, and scalability for large-scale inference tasks. The tool aims to transform the economics of AI inference by delivering fast, sustainable, and scalable AI solutions without compromising on speed or usability.

Salad is a distributed GPU cloud platform that offers fully managed and massively scalable services for AI applications. It provides the lowest priced AI transcription in the market, with features like image generation, voice AI, computer vision, data collection, and batch processing. Salad democratizes cloud computing by leveraging consumer GPUs to deliver cost-effective AI/ML inference at scale. The platform is trusted by hundreds of machine learning and data science teams for its affordability, scalability, and ease of deployment.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

Mystic.ai is an AI tool designed to deploy and scale Machine Learning models with ease. It offers a fully managed Kubernetes platform that runs in your own cloud, allowing users to deploy ML models in their own Azure/AWS/GCP account or in a shared GPU cluster. Mystic.ai provides cost optimizations, fast inference, simpler developer experience, and performance optimizations to ensure high-performance AI model serving. With features like pay-as-you-go API, cloud integration with AWS/Azure/GCP, and a beautiful dashboard, Mystic.ai simplifies the deployment and management of ML models for data scientists and AI engineers.

Groq is a fast AI inference tool that offers GroqCloud™ Platform and GroqRack™ Cluster for developers to build and deploy AI models with ultra-low-latency inference. It provides instant intelligence for openly-available models like Llama 3.1 and is known for its speed and compatibility with other AI providers. Groq powers leading openly-available AI models and has gained recognition in the AI chip industry. The tool has received significant funding and valuation, positioning itself as a strong challenger to established players like Nvidia.

The Cerebras API is a high-speed inferencing solution for AI model inference powered by Cerebras Wafer-Scale Engines and CS-3 systems. It offers developers access to two models: Meta’s Llama 3.1 8B and 70B models, which are instruction-tuned and suitable for conversational applications. The API provides low-latency solutions and invites developers to explore new possibilities in AI development.

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.

fal.ai is a generative media platform designed for developers to build the next generation of creativity. It offers lightning-fast inference with no compromise on quality, providing access to high-quality generative media models optimized by the fal Inference Engine™. The platform allows developers to fine-tune their own models, leverage real-time infrastructure for new user experiences, and scale to thousands of GPUs as needed. With a focus on developer experience, fal.ai aims to be the fastest AI tool for running diffusion models.

Luxonis is an AI application that offers Visual AI solutions engineered for precision edge inference. The application provides stereo depth cameras with unique features and quality, enabling users to perform advanced vision tasks on-device, reducing latency and bandwidth demands. With open-source DepthAI API, users can create and deploy custom vision solutions that scale with their needs. Luxonis also offers real-world training data for self-improving vision intelligence and operates flawlessly through vibrations, temperature shifts, and extended use. The application integrates advanced sensing capabilities with up to 48MP cameras, wide field of view, IMUs, microphones, ToF, thermal, IR illumination, and active stereo for unparalleled perception.

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.

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.

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.

Inworld is an AI framework designed for games and media, offering a production-ready framework for building AI agents with client-side logic and local model inference. It provides tools optimized for real-time data ingestion, low latency, and massive scale, enabling developers to create engaging and immersive experiences for users. Inworld allows for building custom AI agent pipelines, refining agent behavior and performance, and seamlessly transitioning from prototyping to production. With support for C++, Python, and game engines, Inworld aims to future-proof AI development by integrating 3rd-party components and foundational models to avoid vendor lock-in.

NeuReality is an AI-centric solution designed to democratize AI adoption by providing purpose-built tools for deploying and scaling inference workflows. Their innovative AI-centric architecture combines hardware and software components to optimize performance and scalability. The platform offers a one-stop shop for AI inference, addressing barriers to AI adoption and streamlining computational processes. NeuReality's tools enable users to deploy, afford, use, and manage AI more efficiently, making AI easy and accessible for a wide range of applications.

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.

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.

NetMind is an AI tool that offers a Model Library, Enterprise AI Solutions, and AI Consulting services. It provides cutting-edge inference capabilities, model APIs for various data types, and GPU clusters for accelerated performance. The platform allows rapid deployment of models with flexible scaling options. NetMind caters to a wide range of industries, offering solutions that enhance accuracy, cut costs, and accelerate decision-making processes.

PoplarML is a platform that enables the deployment of production-ready, scalable ML systems with minimal engineering effort. It offers one-click deploys, real-time inference, and framework agnostic support. With PoplarML, users can seamlessly deploy ML models using a CLI tool to a fleet of GPUs and invoke their models through a REST API endpoint. The platform supports Tensorflow, Pytorch, and JAX models.

FriendliAI is a generative AI infrastructure company that offers efficient, fast, and reliable generative AI inference solutions for production. Their cutting-edge technologies enable groundbreaking performance improvements, cost savings, and lower latency. FriendliAI provides a platform for building and serving compound AI systems, deploying custom models effortlessly, and monitoring and debugging model performance. The application guarantees consistent results regardless of the model used and offers seamless data integration for real-time knowledge enhancement. With a focus on security, scalability, and performance optimization, FriendliAI empowers businesses to scale with ease.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

CrossRoads is a small package designed to orchestrate agents in Cloudflare Workers and durable objects. It aims to experiment with durable patterns and the interface between humans and AI. The tool leverages workers to execute tool calls at a large scale and utilizes RxJS for easy graph execution. Examples of applications include inference-time scaling with workers, durable-runner, and durable-search. Please note that this project is a work in progress and may experience issues during development.

AIBrix is an open-source initiative providing essential building blocks for scalable GenAI inference infrastructure. It delivers a cloud-native solution optimized for deploying, managing, and scaling large language model (LLM) inference, tailored to enterprise needs. Key features include High-Density LoRA Management, LLM Gateway and Routing, LLM App-Tailored Autoscaler, Unified AI Runtime, Distributed Inference, Distributed KV Cache, Cost-efficient Heterogeneous Serving, and GPU Hardware Failure Detection.

Marqo is more than a vector database, it's an end-to-end vector search engine for both text and images. Vector generation, storage and retrieval are handled out of the box through a single API. No need to bring your own embeddings.

LLM App Stack, also known as Emerging Architectures for LLM Applications, is a comprehensive list of available tools, projects, and vendors at each layer of the LLM app stack. It covers various categories such as Data Pipelines, Embedding Models, Vector Databases, Playgrounds, Orchestrators, APIs/Plugins, LLM Caches, Logging/Monitoring/Eval, Validators, LLM APIs (proprietary and open source), App Hosting Platforms, Cloud Providers, and Opinionated Clouds. The repository aims to provide a detailed overview of tools and projects for building, deploying, and maintaining enterprise data solutions, AI models, and applications.

Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:

The 'vector-search-class-notes' repository contains class materials for a course on Long Term Memory in AI, focusing on vector search and databases. The course covers theoretical foundations and practical implementation of vector search applications, algorithms, and systems. It explores the intersection of Artificial Intelligence and Database Management Systems, with topics including text embeddings, image embeddings, low dimensional vector search, dimensionality reduction, approximate nearest neighbor search, clustering, quantization, and graph-based indexes. The repository also includes information on the course syllabus, project details, selected literature, and contributions from industry experts in the field.

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.

This repository explores the potential of LLMs as coding tutors through the proposed Traver agent workflow. It focuses on incorporating knowledge tracing and turn-by-turn verification to tackle challenges in coding tutoring. The method extends beyond coding to other task-tutoring scenarios, adapting content to users' varying levels of background knowledge. The repository introduces the DICT evaluation protocol for assessing tutor performance through student simulation and coding tests. It also discusses the inference-time scaling with verifiers and provides resources for training and evaluation.

AWQ (Activation-aware Weight Quantization) is a tool designed for efficient and accurate low-bit weight quantization (INT3/4) for Large Language Models (LLMs). It supports instruction-tuned models and multi-modal LMs, providing features such as AWQ search for accurate quantization, pre-computed AWQ model zoo for various LLMs, memory-efficient 4-bit linear in PyTorch, and efficient CUDA kernel implementation for fast inference. The tool enables users to run large models on resource-constrained edge platforms, delivering more efficient responses with LLM/VLM chatbots through 4-bit inference.

This repository contains the official implementation of RPG, a powerful training-free paradigm for text-to-image generation and editing. RPG utilizes proprietary or open-source MLLMs as prompt recaptioner and region planner with complementary regional diffusion. It achieves state-of-the-art results and can generate high-resolution images. The codebase supports diffusers and various diffusion backbones, including SDXL and SD v1.4/1.5. Users can reproduce results with GPT-4, Gemini-Pro, or local MLLMs like miniGPT-4. The repository provides tools for quick start, regional diffusion with GPT-4, and regional diffusion with local LLMs.

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨‍💻‍ Welcome to star ⭐️ or submit a PR to this repo!

KubeAI is a highly scalable AI platform that runs on Kubernetes, serving as a drop-in replacement for OpenAI with API compatibility. It can operate OSS model servers like vLLM and Ollama, with zero dependencies and additional OSS addons included. Users can configure models via Kubernetes Custom Resources and interact with models through a chat UI. KubeAI supports serving various models like Llama v3.1, Gemma2, and Qwen2, and has plans for model caching, LoRA finetuning, and image generation.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

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.

Marlin is a highly optimized FP16xINT4 matmul kernel designed for large language model (LLM) inference, offering close to ideal speedups up to batchsizes of 16-32 tokens. It is suitable for larger-scale serving, speculative decoding, and advanced multi-inference schemes like CoT-Majority. Marlin achieves optimal performance by utilizing various techniques and optimizations to fully leverage GPU resources, ensuring efficient computation and memory management.

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