Top 50 Gpu As A Service - Late Stage

Lambda provides an on‑demand supercomputing platform that lets AI teams provision private, single‑tenant GPU clusters with the latest NVIDIA GB300, B200, and H200 accelerators via a web console or API. The service offers up to 64‑GPU nodes with NVLink and InfiniBand interconnects, SOC 2 Type II security, and pay‑as‑you‑go per‑GPU‑hour billing, enabling scalable training and inference for research labs and enterprise ML teams.

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.

CoreWeave provides an AI-native cloud platform built on next-generation infrastructure, purpose-built for complex AI workloads. The platform offers specialized GPU compute, storage, and high-performance networking within a Kubernetes-native environment. This specialized offering accelerates AI development cycles, training, and inference with enhanced efficiency and operational control.

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.

GMI Cloud provides instant access to NVIDIA H100 GPUs for training and deploying generative AI applications, utilizing a Kubernetes-based cluster engine for efficient workload orchestration. This platform addresses the need for rapid GPU provisioning and management, enabling developers to focus on building AI models without the complexities of infrastructure setup.

Vultr provides cloud infrastructure with dedicated clusters and on-demand virtual machines powered by AMD and NVIDIA GPUs, enabling efficient deployment of AI and high-performance computing workloads. The platform offers scalable solutions at competitive pricing, addressing the need for accessible and powerful computing resources for developers and businesses globally.

Crusoe provides a managed AI cloud platform that delivers low‑latency, high‑throughput inference for large‑context models using NVIDIA and AMD GPUs with its MemoryAlloy engine. The service abstracts cluster provisioning via an API‑key workflow, auto‑scales on Kubernetes/Slurm, and includes a web console for one‑click model deployment, while its renewable‑powered data centers reduce compute costs by up to 80 %.

Foundry provides an orchestration platform that enables AI developers to access NVIDIA GPU clusters on-demand, facilitating training, fine-tuning, and inference without long-term contracts. The platform addresses the challenge of unpredictable compute needs by offering flexible pricing options, including reserved and spot instances, ensuring reliable performance for critical workloads.

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.

Provides bare-metal access to high-performance AI compute infrastructure powered by NVIDIA HGX H100 GPUs and a 3200 Gbps InfiniBand network, enabling low-latency, scalable training and inference for large-scale machine learning models. Offers transparent pricing and flexible deployment options, including on-demand nodes and long-term contracts, to meet the needs of demanding workloads in AI, HPC, and real-time applications.

Firmus provides a modular AI infrastructure platform that combines liquid‑cooled, high‑density GPU clusters with a public AI cloud offering on‑demand and reserved GPU instances. Its AI FactoryOS orchestration layer automates workload placement, power and cooling management, and delivers real‑time telemetry, enabling AI labs and enterprise teams to train large models efficiently and predictably.

Firmus provides a modular AI infrastructure platform that combines liquid‑cooled, high‑density GPU clusters with a public AI cloud offering on‑demand and reserved GPU instances. Its AI FactoryOS orchestration layer automates workload placement, power and cooling management, and delivers real‑time telemetry, enabling AI labs and enterprise teams to train large models efficiently and predictably.

Together AI provides a cloud platform that offers serverless OpenAI‑compatible inference APIs for over 200 open‑source models, accelerated up to 4× by its ATLAS runtime. Users can provision on‑demand or reserved NVIDIA GPU clusters for fine‑tuning and batch inference, with per‑token or hourly usage pricing and enterprise‑grade security.

Together AI provides an AI-native cloud platform engineered for accelerating model training, fine-tuning, and inference on performance-optimized GPU infrastructure. The platform offers a comprehensive suite of tools, including a model library, serverless inference APIs, and self-service GPU clusters featuring frontier hardware. This infrastructure delivers industry-leading unit economics and performance for developers building large-scale generative AI applications.

Exafunction optimizes GPU workloads by relocating code execution to remote resources while maintaining core logic on cost-effective CPU instances. This approach reduces operational costs and enhances computational efficiency for businesses reliant on high-performance computing.

Luminary Cloud provides a simulation platform that utilizes Physics AI for real-time engineering design, enabling users to run high-fidelity simulations in minutes on cloud-based GPUs. This technology eliminates the need for extensive hardware and installation, significantly reducing prototyping costs and accelerating product development cycles.

Luminary Cloud offers a cloud‑native platform that runs GPU‑accelerated CFD and multiphysics solvers, allowing engineers to generate thousands of high‑resolution simulations in minutes. The platform includes an integrated notebook for data preparation, Physics‑AI model training, and visualization, and provides a secure REST API that returns performance predictions in seconds for use in CAD/CAE workflows. Built‑in adjoint sensitivity analysis enables gradient‑based multi‑objective optimization across large design spaces.

Cerebras Systems provides a wafer‑scale AI processor that offers vastly higher memory bandwidth and lower latency than traditional GPUs, allowing developers to train and serve models from 1 B to 24 T parameters without sharding or code changes. The platform is available via cloud, private‑cloud API, or on‑premise deployment with OpenAI‑compatible endpoints and usage‑based pricing.

Cerebras provides a wafer‑scale AI compute platform that runs inference, fine‑tuning, and full‑parameter training of large language models on a single engine, delivering up to 3,000 tokens per second and reducing total cost of ownership versus GPU clusters. The system is offered as on‑premise CS‑2/CS‑3 hardware, private‑cloud capacity, or a pay‑as‑you‑go SaaS, with a drop‑in OpenAI‑compatible API and SOC 2/HIPAA‑certified data handling for enterprise workloads.

Bitdeer provides digital asset mining services utilizing high-performance computing to optimize the mining process for cryptocurrencies. The company enables clients to efficiently access and manage mining resources, reducing operational costs and increasing profitability in the competitive blockchain landscape.

Volumez offers a Data Infrastructure as a Service (DIaaS) platform that dynamically orchestrates compute, network, and storage resources across cloud environments to create optimized data infrastructures for various workloads. This solution addresses the challenges of performance inconsistency and resource inefficiency in data-intensive applications by delivering guaranteed high throughput, low latency, and maximized GPU utilization.

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.

Anyscale provides a configurable AI platform powered by RayTurbo, enabling developers to optimize and scale AI applications across any cloud and hardware configuration. The platform enhances GPU utilization and reduces cloud costs by up to 50%, facilitating faster model training and deployment for complex AI workloads.

Voltron Data provides Theseus, a GPU-accelerated SQL engine designed for processing petabyte-scale data without the need for indexing or data movement. It enables enterprises to significantly reduce query times, server counts, and operational costs, making it ideal for large-scale ETL and machine learning preprocessing tasks.

Core Scientific provides high‑density colocation data centers with a minimum of 30 MW per site and up to 200 kW per cabinet, featuring GPU‑optimized racks, direct liquid‑cooling, and carrier‑neutral high‑bandwidth fiber. The facilities deliver over 1.3 GW of contracted power across the United States, with 24 × 7 NOC monitoring, flexible turnkey provisioning, and managed power services to support AI/ML, hyperscale, and other compute‑intensive workloads. This enables customers to scale high‑performance compute without building their own infrastructure, lowering total cost of ownership and deployment time.

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.

The startup has developed a decentralized video delivery network that enables users to earn rewards by relaying video content through their excess bandwidth on any device. This platform supports the creation of decentralized applications for esports, entertainment, and peer-to-peer streaming, helping organizations enhance viewer engagement and lower operational costs.

Rescale provides a cloud-based high-performance computing platform that enables scientific and engineering simulations with customizable resources tailored to specific workloads. This platform reduces turnaround times and enhances data insights, allowing organizations to optimize their research and development processes efficiently.

Pika provides a cloud‑based generative AI platform that turns text prompts or static images into fully rendered video clips using diffusion‑based synthesis. Users can generate 720p‑4K videos in minutes via an intuitive web UI or API, with style presets and real‑time previews, eliminating the need for manual editing. The service scales GPU‑accelerated rendering for creators, marketers, and agencies seeking rapid, high‑quality visual content.

Fireworks AI provides a serverless inference platform that enables the rapid deployment and fine-tuning of compound AI models, optimizing for speed and cost efficiency. The technology addresses the challenges of slow model inference and high operational costs, allowing businesses to scale AI applications effectively while maintaining low latency and high throughput.

Groq accelerates AI inference with custom-designed Language Processing Units (LPUs) that deliver sub-millisecond latency and consistent performance. Their cloud platform and on-premise solutions enable developers to deploy AI models efficiently and cost-effectively.

Baseten provides a platform for deploying and serving machine learning models with optimized inference speed and autoscaling capabilities, enabling seamless transition from development to production. The solution addresses the complexities of model infrastructure management, allowing teams to focus on building and iterating on their AI applications without incurring excessive costs.

XtalPi provides an AI‑driven, quantum‑mechanics‑based platform that integrates generative molecule design, GPU‑accelerated free‑energy perturbation, and automated crystallization to predict binding affinities and solid‑state forms. The system links digital chemistry, robotic synthesis, and high‑throughput screening in a cloud‑hosted workflow, enabling pharmaceutical, biotech, and materials companies to shorten discovery cycles, lower R&D costs, and improve candidate success rates.

Saronic offers a cloud‑native AI platform that centralizes the full machine‑learning lifecycle for enterprise teams. It provides auto‑scaling compute for distributed training, automated data‑ingestion and feature‑store pipelines, version‑controlled model management, and secure inference APIs with built‑in explainability and audit logging. The platform integrates with major data warehouses, enabling data‑science and analytics groups to deploy predictive models at scale while maintaining governance and compliance.

Style3D provides a cloud‑based platform that uses generative AI to turn 2D fashion sketches into fully simulated 3D garments, automatically generating patterns, trim specs, and photorealistic renders. GPU‑accelerated deformable body simulation enables fit assessment without physical samples, while a centralized asset library with version control supports real‑time collaboration, style tracking, and inventory visibility for designers, brands, and manufacturers.

Baseten provides an inference platform that lets ML teams deploy and manage large language, diffusion, transcription, and other generative AI models with a single click. The service offers pre‑optimized runtimes, automatic multi‑cloud capacity management, and built‑in high‑availability, while supporting single‑tenant or self‑hosted deployments for secure, low‑latency serving. It integrates with CI/CD pipelines via API/SDK and includes tools for version control, monitoring, and performance tuning.

Etched.ai develops Sohu, the world's first ASIC specifically designed for transformer models, enabling AI computations to be executed at least ten times faster and more cost-effectively than traditional GPUs. This technology allows for real-time processing of large-scale AI models, enhancing applications such as voice agents and content generation.

MangoBoost develops Data Processing Units (DPUs) that enhance data center performance by offloading network and storage tasks, resulting in reduced latency and improved efficiency. Their solutions address the challenges of high network traffic and storage management, enabling scalable and cost-effective AI infrastructure.

The startup offers a machine-learning community platform that facilitates collaboration on models, datasets, and applications, enabling users to create and discover machine-learning projects. By providing paid computing resources and enterprise systems, the platform enhances the efficiency of open-source development, allowing users to contribute to and advance the field of machine learning.

Fly.io provides a public cloud infrastructure with hardware-virtualized containers called Fly Machines, which can boot in under 250 milliseconds and scale to tens of thousands of instances. This platform enables developers to deploy applications globally with sub-100ms response times, addressing the need for fast, flexible, and cost-effective compute resources without the complexities of traditional server management.

This company provides accelerated computing platforms and software solutions for AI, high-performance computing, and data centers. They offer specialized hardware like GPUs and integrated systems to power demanding workloads across various industries. Their technology enables advancements in areas ranging from autonomous vehicles and robotics to scientific visualization and generative AI development.

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.

Habana Labs develops Intel® Gaudi® AI accelerators designed for high-performance deep learning training and inference, providing enterprises and cloud providers with efficient compute solutions. Their technology delivers up to 40% better price/performance on cloud instances, addressing the need for cost-effective and scalable AI infrastructure.

MatX manufactures specialized hardware designed for training and inference of large AI models, delivering up to 10× more computing power for workloads with over 7 billion parameters. This enables researchers and startups to efficiently train advanced models, significantly reducing the time and cost associated with developing state-of-the-art AI systems.

Astera Labs develops semiconductor-based connectivity solutions, including PCIe over optics and smart fabric switches, to enhance the scalability and efficiency of AI and cloud infrastructure. Their technology addresses the challenge of optimizing resource management in data centers, enabling higher GPU cluster utilization and improved performance.

The startup develops high-density computing infrastructure specifically designed for AI processing, enabling efficient operationalization of machine learning and compute-intensive workloads. Their platform offers environmentally responsible data processing, allowing clients to achieve faster results while minimizing carbon emissions.

Xoda provides a decentralized AI platform integrating Blockchain, IPFS, and LLMs to enable secure research, analysis, and development. The platform supports AI model developers with tools for building and monetization, and application developers in creating new AI-powered solutions. Resource providers can contribute compute power to the ecosystem while users benefit from transparent and autonomous AI development.

NextSilicon's Maverick-2 Intelligent Compute Accelerator (ICA) utilizes software-defined hardware to dynamically optimize performance for high-performance computing (HPC) and artificial intelligence (AI) workloads. This technology eliminates the need for extensive code rewrites, significantly reducing development time and enabling faster insights across various applications.

LOCI is an AI‑driven observability platform that analyzes compiled CPU and GPU binaries, using a hardware‑aware large code language model to predict performance and power hotspots before test or inference runs. It automatically rewrites binaries and adjusts runtime configurations, integrating with CI/CD pipelines to provide measurable throughput and energy savings for AI/ML and performance engineering teams.

Enflame develops cloud-based deep learning chips specifically designed for AI training platforms, enhancing computational efficiency and speed. This technology addresses the high resource demands of AI model training, enabling faster iterations and reduced operational costs for businesses.