Top 50 Ai Gpu Cloud in Europe

Genesis Cloud provides a GPU cloud platform built on NVIDIA's reference architecture, delivering up to 35 times more performance for AI and machine learning workloads at 80% lower costs compared to traditional cloud providers. The platform ensures high security and compliance with EU regulations, enabling enterprises to efficiently manage and scale their AI applications.

NexGen Cloud provides sustainable Infrastructure as a Service (IaaS) with a focus on high-performance computing (HPC) and GPU infrastructure, utilizing its Hyperstack platform for on-demand GPU as a Service (GPUaaS). The company enables businesses to efficiently integrate AI capabilities into their operations while ensuring data privacy and compliance through its European and North American data centers.

PERIAN provides a serverless Sky Computing Platform that offers unified, on-demand access to a wide range of GPU resources across multiple cloud providers, enabling efficient management and deployment of GPU workloads. The platform's cost optimization features ensure users consistently access the most competitive pricing, resulting in potential savings of up to 87% on cloud computing expenses.

Provides a fully managed AI cloud platform powered by NVIDIA® H100 and H200 Tensor Core GPUs, offering scalable GPU clusters with InfiniBand networking for high-speed data processing. Enables efficient model training, fine-tuning, and inference with tools like MLflow, PostgreSQL, and Apache Spark, reducing the complexity and cost of deploying AI applications at scale.

Nscale provides a GPU cloud platform optimized for AI workloads, featuring on-demand compute and inference services, dedicated training clusters, and scalable GPU nodes. The platform addresses the high costs and inefficiencies associated with AI model training and deployment by offering a fully integrated infrastructure powered by renewable energy in Europe.

FluidStack provides on-demand access to thousands of NVIDIA A100 and H100 GPUs, enabling AI engineers to rapidly scale their training and inference workloads without long-term contracts. The platform offers fully managed GPU clusters with 24/7 support, significantly reducing operational overhead and accelerating model deployment.

Ori provides on-demand access to top-tier GPUs and serverless Kubernetes for training and deploying machine learning models at scale. The platform offers cost-optimized solutions that allow users to pay only for the resources they utilize, addressing the need for flexible and efficient AI infrastructure.

DataCrunch provides on-demand access to high-performance GPU instances and custom-built clusters powered by NVIDIA H200 and H100 technology, enabling efficient model inference and training for machine learning applications. The platform utilizes 100% renewable energy, offering a scalable solution that reduces the infrastructure burden for businesses deploying AI models.

FlexAI provides a universal AI compute platform that enables developers to run AI workloads across diverse hardware architectures without code modifications. This approach maximizes resource utilization and energy efficiency, reducing operational complexity and minimizing failures in AI product development.

SpiNNcloud Systems develops specialized hardware that replicates brain-like parallel processing to enhance real-time computing for complex simulations and data analysis. This technology overcomes the limitations of traditional computing architectures, significantly improving efficiency in handling large-scale data tasks.

Avesha provides a technology platform that enables efficient management and orchestration of application workloads across cloud, multi-cloud, and edge environments using predictive algorithms and automated scaling. The platform addresses high Kubernetes costs and inefficient GPU utilization by optimizing resource allocation and performance in real-time.

Cudos provides a decentralized cloud computing network that allows users to deploy virtual machines and write smart contracts using a globally distributed resource pool. This platform addresses the need for cost-effective, secure, and scalable infrastructure for decentralized applications by enabling permissionless access through digital wallets.

Stelia provides a data mobility platform that facilitates seamless data transfer and accessibility across diverse AI infrastructures. This technology eliminates data silos and enhances data management, enabling organizations to optimize AI workflows and improve decision-making efficiency.

The startup offers a photonic computing platform designed to enable the development of artificial general intelligence (AGI) at scale. This technology addresses the limitations of traditional computing by providing a more efficient and sustainable infrastructure for complex AI computations.

RaiderChip designs semiconductor hardware accelerators that enhance AI performance by addressing memory bandwidth limitations. Their solutions enable efficient AI inference for both edge and cloud applications, allowing users to run complex large language models locally with full privacy and without ongoing subscriptions.

Gcore provides cloud and edge computing solutions that enhance content delivery, hosting, and security for businesses. By optimizing data transfer and storage, Gcore addresses latency and security challenges faced by companies operating in a digital landscape.

Axelera AI manufactures AI acceleration hardware, specifically the Metis AI Processing Unit (AIPU), designed for efficient edge computing with up to 214 TOPS performance and 15 TOPS per watt. The technology addresses the need for cost-effective and energy-efficient solutions in generative AI and computer vision applications across various industries, including retail and security.

The startup manufactures semiconductor chips with a multicore DSP architecture that accelerates the design of complex integrated circuits for mobile and network infrastructure. By eliminating the need for DSP coprocessors, these chips enable chipmakers to efficiently develop next-generation digital communication systems, including fifth-generation technologies.

The startup offers a cloud platform that streamlines development processes by automating environment management and hosting, allowing developers to focus more on coding. This solution reduces infrastructure costs and minimizes the time spent on operational tasks, enhancing overall productivity.

Kalray offers high-performance processing acceleration solutions powered by its MPPA® architecture. These solutions efficiently handle data-intensive workloads in AI, automotive, and telecommunications, delivering superior performance and energy efficiency for demanding applications.

DePIN is a decentralized compute network that utilizes the processing power of millions of smartphones, desktops, and data centers to provide low-cost AI processing. This infrastructure enables companies to scale their AI applications efficiently without incurring high computational costs.

The startup has developed a protocol tailored for the computational demands of global deep learning models in machine learning. This technology enhances processing efficiency and scalability, addressing the challenges of resource-intensive AI applications.

The startup develops machine learning tools that provide a zero-configuration Python API for running open-source simulations and optimization tools in scientific research. This technology eliminates technical barriers in computational tools, enabling scientists and engineers to efficiently conduct complex analyses both on-premise and in the cloud.

SKY ENGINE AI provides a Synthetic Data Cloud that generates multimodal synthetic data for training deep learning models in computer vision, significantly reducing the need for real-world image acquisition. This technology enhances model accuracy by up to 4150% and accelerates AI development cycles by up to 3340 times, addressing the challenges of data scarcity and high costs in various industries such as automotive, healthcare, and robotics.

The startup develops ultra-low power AI solutions for edge devices, enabling real-time data processing without reliance on cloud infrastructure. This technology addresses the need for energy-efficient smart devices in environments with limited power availability.

InstaDeep develops AI-powered decision-making systems utilizing GPU-accelerated computing, deep learning, and reinforcement learning to tackle complex challenges in industries such as logistics, energy, and biology. Their technology enhances operational efficiency and precision, enabling enterprises to make data-driven decisions in an increasingly AI-centric landscape.

Gensyn is a machine learning compute protocol that connects distributed resources to facilitate the training of deep learning models. This approach addresses the need for open, permissionless, and neutral frameworks that enable efficient scaling and collaboration in machine intelligence development.

Aiven provides a unified platform for managing open-source data infrastructure across multiple cloud environments, enabling businesses to deploy and optimize databases, streaming, and analytics with minimal setup time. This solution addresses the complexity and unpredictability of cloud data management, allowing organizations to focus on application development while ensuring compliance and cost transparency.

Multiverse Computing develops quantum software solutions that enhance AI and optimization processes, specifically targeting the high computational costs associated with training large language models and optimizing renewable energy systems. Their platform, Singularity, provides tailored algorithms that improve efficiency, reduce costs, and support sustainable practices across various industries.

Leafcloud offers sustainable cloud infrastructure by repurposing server waste heat to warm urban buildings, reducing environmental impact and operational costs. They provide core cloud services like VMs and GPUs on a distributed network of urban sites, built on open-source technology for data sovereignty and flexibility.

The startup develops a neuromorphic chip that mimics human brain information-processing mechanisms to enhance artificial intelligence hardware. This technology addresses computing bottlenecks by enabling more efficient training of neural networks for AI applications.

Pruna AI provides an AI optimization engine that enhances machine learning model performance with just two lines of code, utilizing execution kernel and graph optimization techniques. This solution reduces runtime costs and carbon emissions by making AI models faster and more efficient, enabling scalable inference without extensive re-engineering.

deepset provides an open-source framework, Haystack, and a cloud platform for enterprises to develop and deploy custom applications using large language models (LLMs). This technology enables organizations to efficiently prototype, test, and launch AI-driven solutions that enhance data processing and improve decision-making across various business functions.

Runware provides an ultra-fast API for generative media, utilizing custom hardware and renewable energy to deliver image generation at sub-second speeds and costs as low as $0.0006 per image. The platform eliminates the need for specialized infrastructure or machine learning expertise, enabling users to access over 180,000 open-source models and seamlessly integrate AI content generation into their applications.

TitanML provides an enterprise-grade LLM cluster for high-performance language model inference, enabling organizations to deploy AI applications securely within their own infrastructure. This solution addresses the need for data privacy and control while optimizing operational costs and performance through advanced inference techniques.

Qoro develops network software that integrates quantum and classical computing systems, enabling efficient resource sharing across diverse hardware, including GPU clusters and quantum computers. The platform automates quantum program development, eliminating hardware-specific complexities for users while providing a robust network stack for hardware providers.

NetMind offers a unified platform for accessing and deploying diverse AI models, including LLMs and multimodal capabilities, through standard APIs and the Model Context Protocol. The service simplifies AI infrastructure by providing on-demand GPU cluster rentals and managed inference endpoints, enabling developers to integrate AI without managing complex deployments.

The startup provides a compute-intensive platform that enables organizations to optimize resource allocation for their workloads across on-premise, hybrid-cloud, and multi-cloud environments. By utilizing a sophisticated scheduler, the platform ensures efficient computing and resource utilization from any cloud provider and region simultaneously.

The startup develops decentralized artificial intelligence networks that enable developers to create scalable AI infrastructure with improved performance compared to centralized systems. This technology allows companies to enhance their AI capabilities and reach while reducing reliance on traditional network architectures.

The startup provides cloud services, including infrastructure as a service and platform as a service, tailored for sectors such as healthcare, defense, energy, and banking. Their solutions enable organizations to enhance operational efficiency and scalability while managing complex cloud strategies effectively.

UbiOps provides a unified MLOps platform that enables the deployment and management of AI workloads across local, hybrid, and multi-cloud environments. By streamlining AI operations with built-in features like version control and automatic resource scaling, UbiOps reduces infrastructure overhead and development costs by up to 80%.

The startup develops deep learning technology that integrates machine learning capabilities into electronic devices and robots, enhancing their computational power and connectivity. This technology enables the creation of smart products that perform tasks efficiently, improving user safety and convenience.

gridscale provides a user-friendly platform for creating and managing private cloud infrastructures through intuitive drag-and-drop web interfaces, enabling businesses to deploy complex IT environments in minutes. The service addresses the challenges of scalability, security, and vendor lock-in by offering flexible, on-demand resources with European data protection compliance.

dstack is an open-source orchestration engine that simplifies the management of AI workloads across cloud and on-premises environments, supporting various hardware accelerators like NVIDIA and TPU. dstack Sky provides a global marketplace for affordable GPUs, enabling AI engineers to access cost-effective computing resources without the high premiums typically associated with major cloud providers.

Lumai develops a 3D optical processor that significantly enhances AI performance in data centers while achieving a 90% reduction in power consumption compared to traditional silicon-based solutions. This technology addresses the escalating demand for AI processing power by providing a scalable, energy-efficient alternative that lowers both capital and operational costs.

Develops cloud management software optimized for ARM, RISC-V, and Intel architectures to improve data center resource allocation and reduce energy consumption. By integrating AI-powered analytics, smart scheduling, and automated workload management, it enables data centers to lower operational costs and minimize their carbon footprint. The platform supports migration to energy-efficient processors and provides tools for real-time performance monitoring and optimization.

This company provides a cloud platform with data analytics, AI automation, and infrastructure management tools to help businesses modernize their operations. Their platform enables businesses to accelerate digital transformation, optimize processes, and improve agility.

The startup develops AI-driven software that automates the design and training of neural networks for artificial vision applications. This platform enables companies to deploy optimized vision models that achieve high accuracy while minimizing power consumption.

Celestical provides a GDPR-compliant cloud computing platform optimized for AI LLM workloads and general applications. It offers high-performance infrastructure with predictable costs and automated server operations for efficient development, deployment, and scaling.