Best AI tools for< Deploy Gpu >

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.

Nomi.cloud is a modern AI-powered CloudOps and HPC assistant designed for next-gen businesses. It offers developers, marketplace, enterprise solutions, and pricing console. With features like single pane of glass view, instant deployment, continuous monitoring, AI-powered insights, and budgets & alerts built-in, Nomi.cloud aims to revolutionize cloud management. It provides a user-friendly interface to manage infrastructure efficiently, optimize costs, and deploy resources across multiple regions with ease. Nomi.cloud is built for scale, trusted by enterprises, and offers a range of GPUs and cloud providers to suit various needs.

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.

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.

GPUX is a cloud platform that provides access to GPUs for running AI workloads. It offers a variety of features to make it easy to deploy and run AI models, including a user-friendly interface, pre-built templates, and support for a variety of programming languages. GPUX is also committed to providing a sustainable and ethical platform, and it has partnered with organizations such as the Climate Leadership Council to reduce its carbon footprint.

Cerebium is a serverless AI infrastructure platform that allows teams to build, test, and deploy AI applications quickly and efficiently. With a focus on speed, performance, and cost optimization, Cerebium offers a range of features and tools to simplify the development and deployment of AI projects. The platform ensures high reliability, security, and compliance while providing real-time logging, cost tracking, and observability tools. Cerebium also offers GPU variety and effortless autoscaling to meet the diverse needs of developers and businesses.

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.

Novita AI is an AI cloud platform offering Model APIs, Serverless, and GPU Instance services in a cost-effective and integrated manner to accelerate AI businesses. It provides optimized models for high-quality dialogue use cases, full spectrum AI APIs for image, video, audio, and LLM applications, serverless auto-scaling based on demand, and customizable GPU solutions for complex AI tasks. The platform also includes a Startup Program, 24/7 service support, and has received positive feedback for its reasonable pricing and stable services.

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.

Nebius AI is an AI-centric cloud platform designed to handle intensive workloads efficiently. It offers a range of advanced features to support various AI applications and projects. The platform ensures high performance and security for users, enabling them to leverage AI technology effectively in their work. With Nebius AI, users can access cutting-edge AI tools and resources to enhance their projects and streamline their workflows.

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.

VeroCloud is a platform offering tailored solutions for AI, HPC, and scalable growth. It provides cost-effective cloud solutions with guaranteed uptime, performance efficiency, and cost-saving models. Users can deploy HPC workloads seamlessly, configure environments as needed, and access optimized environments for GPU Cloud, HPC Compute, and Tally on Cloud. VeroCloud supports globally distributed endpoints, public and private image repos, and deployment of containers on secure cloud. The platform also allows users to create and customize templates for seamless deployment across computing resources.

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.

GrapixAI is a leading provider of low-cost cloud GPU rental services and AI server solutions. The company's focus on flexibility, scalability, and cutting-edge technology enables a variety of AI applications in both local and cloud environments. GrapixAI offers the lowest prices for on-demand GPUs such as RTX4090, RTX 3090, RTX A6000, RTX A5000, and A40. The platform provides Docker-based container ecosystem for quick software setup, powerful GPU search console, customizable pricing options, various security levels, GUI and CLI interfaces, real-time bidding system, and personalized customer support.

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.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

Paperspace is an AI tool designed to develop, train, and deploy AI models of any size and complexity. It offers a cloud GPU platform for accelerated computing, with features such as GPU cloud workflows, machine learning solutions, GPU infrastructure, virtual desktops, gaming, rendering, 3D graphics, and simulation. Paperspace provides a seamless abstraction layer for individuals and organizations to focus on building AI applications, offering low-cost GPUs with per-second billing, infrastructure abstraction, job scheduling, resource provisioning, and collaboration tools.

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.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

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.

This repository showcases how to build a simple LLM-based chatbot for answering questions based on documents using retrieval augmented generation (RAG) technique. It also provides guidance on deploying the chatbot using Podman or on the OpenShift Container Platform. The workshop associated with this repository introduces participants to LLMs & RAG concepts and demonstrates how to customize the chatbot for specific purposes. The software stack relies on open-source tools like streamlit, LlamaIndex, and local open LLMs via Ollama, making it accessible for GPU-constrained environments.

runpod-worker-comfy is a serverless API tool that allows users to run any ComfyUI workflow to generate an image. Users can provide input images as base64-encoded strings, and the generated image can be returned as a base64-encoded string or uploaded to AWS S3. The tool is built on Ubuntu + NVIDIA CUDA and provides features like built-in checkpoints and VAE models. Users can configure environment variables to upload images to AWS S3 and interact with the RunPod API to generate images. The tool also supports local testing and deployment to Docker hub using Github Actions.

Kalavai is an open-source platform that transforms everyday devices into an AI supercomputer by aggregating resources from multiple machines. It facilitates matchmaking of resources for large AI projects, making AI hardware accessible and affordable. Users can create local and public pools, connect with the community's resources, and share computing power. The platform aims to be a management layer for research groups and organizations, enabling users to unlock the power of existing hardware without needing a devops team. Kalavai CLI tool helps manage both versions of the platform.

Green-Bit-LLM is a Python toolkit designed for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit Language Models (LLMs). It utilizes the Bitorch Engine for efficient operations on low-bit LLMs, enabling high-performance inference on various GPUs and supporting full-parameter fine-tuning using quantized LLMs. The toolkit also provides evaluation tools to validate model performance on benchmark datasets. Green-Bit-LLM is compatible with AutoGPTQ series of 4-bit quantization and compression models.

Beta9 is an open-source platform for running scalable serverless GPU workloads across cloud providers. It allows users to scale out workloads to thousands of GPU or CPU containers, achieve ultrafast cold-start for custom ML models, automatically scale to zero to pay for only what is used, utilize flexible distributed storage, distribute workloads across multiple cloud providers, and easily deploy task queues and functions using simple Python abstractions. The platform is designed for launching remote serverless containers quickly, featuring a custom, lazy loading image format backed by S3/FUSE, a fast redis-based container scheduling engine, content-addressed storage for caching images and files, and a custom runc container runtime.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

CB-Tumblebug (CB-TB) is a system for managing multi-cloud infrastructure consisting of resources from multiple cloud service providers. It provides an overview, features, and architecture. The tool supports various cloud providers and resource types, with ongoing development and localization efforts. Users can deploy a multi-cloud infra with GPUs, enjoy multiple LLMs in parallel, and utilize LLM-related scripts. The tool requires Linux, Docker, Docker Compose, and Golang for building the source. Users can run CB-TB with Docker Compose or from the Makefile, set up prerequisites, contribute to the project, and view a list of contributors. The tool is licensed under an open-source license.

Azure HPC On-Demand Platform (az-hop) provides an end-to-end deployment mechanism for a base HPC infrastructure on Azure. It delivers a complete HPC cluster solution ready for users to run applications, which is easy to deploy and manage for HPC administrators. az-hop leverages various Azure building blocks and can be used as-is or easily customized and extended to meet any uncovered requirements. Industry-standard tools like Terraform, Ansible, and Packer are used to provision and configure this environment, which contains: - An HPC OnDemand Portal for all user access, remote shell access, remote visualization access, job submission, file access, and more - An Active Directory for user authentication and domain control - Open PBS or SLURM as a Job Scheduler - Dynamic resources provisioning and autoscaling is done by Azure CycleCloud pre-configured job queues and integrated health-checks to quickly avoid non-optimal nodes - A Jumpbox to provide admin access - A common shared file system for home directory and applications is delivered by Azure Netapp Files - Grafana dashboards to monitor your cluster - Remote Visualization with noVNC and GPU acceleration with VirtualGL

NVIDIA Generative AI Examples are state-of-the-art examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs. These examples showcase the capabilities of NVIDIA's Generative AI platform, which includes tools, frameworks, and models for building and deploying generative AI applications.

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome). It is designed to provide high performance and scalability for large language models, enabling efficient inference on cloud-based TPUs. JetStream leverages XLA to optimize the execution of LLM models, resulting in faster and more efficient inference. Additionally, JetStream supports quantization techniques to further enhance performance and reduce memory consumption. By utilizing JetStream, developers can deploy and run LLM models on TPUs with ease, achieving optimal performance and cost-effectiveness.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

HelixML is a private GenAI platform that allows users to deploy the best of open AI in their own data center or VPC while retaining complete data security and control. It includes support for fine-tuning models with drag-and-drop functionality. HelixML brings the best of open source AI to businesses in an ergonomic and scalable way, optimizing the tradeoff between GPU memory and latency.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

Gradient CLI is a tool designed to facilitate the end-to-end MLOps process, allowing individuals and organizations to develop, train, and deploy Deep Learning models efficiently. It supports various ML/DL frameworks and provides features such as 1-click Jupyter Notebooks, scalable model training workflows, and model deployment as API endpoints. The tool can run on different infrastructures like AWS, GCP, on-premise, and Paperspace GPUs, offering automatic versioning, distributed training, hyperparameter search, and more.

Netsaur is a powerful machine learning library for Deno, offering a lightweight and easy-to-use neural network solution. It is blazingly fast and efficient, providing a simple API for creating and training neural networks. Netsaur can run on both CPU and GPU, making it suitable for serverless environments. With Netsaur, users can quickly build and deploy machine learning models for various applications with minimal dependencies. This library is perfect for both beginners and experienced machine learning practitioners.

GPT4All is an ecosystem to run powerful and customized large language models that work locally on consumer grade CPUs and any GPU. Note that your CPU needs to support AVX or AVX2 instructions. Learn more in the documentation. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.

FinalRip is a distributed video processing tool based on FFmpeg and VapourSynth. It cuts the original video into multiple clips, processes each clip in parallel, and merges them into the final video. Users can deploy the system in a distributed way, configure settings via environment variables or remote config files, and develop/test scripts in the vs-playground environment. It supports Nvidia GPU, AMD GPU with ROCm support, and provides a dashboard for selecting compatible scripts to process videos.

LLamaSharp is a cross-platform library to run 🦙LLaMA/LLaVA model (and others) on your local device. Based on llama.cpp, inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLM (Large Language Model) in your application with LLamaSharp.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

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