Top 50 Ai Gpu Cloud in Asia

Aethirs provides a decentralized cloud infrastructure that delivers on-demand access to enterprise-grade GPUs for AI model training and real-time gaming applications. This solution addresses the need for scalable, low-latency compute resources while ensuring high performance and security across a global network.

Provides a decentralized platform that aggregates idle GPU resources from data centers and independent providers worldwide, creating a scalable and cost-effective infrastructure for on-demand high-performance computing. This system addresses the shortage of AI-grade GPUs by enabling seamless access to thousands of GPUs, including H100s and A6000s, for applications like AI training, rendering, and scientific computation.

KAE Systems offers an Industry Cloud Platform that utilizes containerization and Kubernetes orchestration to provide GPU-based development environments for AI-driven robotics and computer vision applications. This platform enables teams to rapidly deploy and scale their solutions without the need for complex infrastructure, significantly reducing development time and costs.

The startup operates a cloud-based computing platform that provides AI-driven solutions for researchers and enterprises, focusing on large language model development, programmatic data labeling, and machine learning testing. It offers high-performance computing resources, including access to powerful GPUs and virtual machines, while promoting e-waste reduction through environmentally friendly practices.

Inferless provides a serverless GPU platform that enables rapid deployment of custom machine learning models from various sources, including Hugging Face and Docker, while automatically scaling resources to handle unpredictable workloads. This solution reduces operational costs by up to 90% and eliminates the complexities associated with traditional GPU clusters, allowing businesses to efficiently manage their machine learning inference needs.

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.

Chip Render Farm provides an Infrastructure as a Service (IaaS) cloud rendering solution optimized for CPU and GPU rendering across various 3D applications, including Unreal Engine and Blender. It enables 3D artists to efficiently manage rendering tasks at a low cost while maintaining complete control over their workflows and project environments.

The startup offers a blockchain-based platform that enables global sharing and utilization of GPU resources. This approach addresses the inefficiency of underutilized computing power, allowing users to access and monetize excess GPU capacity effectively.

G42 is an artificial intelligence and cloud computing company that delivers tailored AI solutions across various industries to enhance operational efficiency and data management. By leveraging advanced machine learning algorithms and scalable cloud infrastructure, G42 addresses the challenges of data processing and analytics in complex business environments.

Kluisz.ai provides an AI-powered cloud platform for deploying and managing enterprise workloads across on-premises, edge, and hybrid environments. Its API-first architecture and zero-trust security principles enable seamless application composability and unified management for distributed computing resources.

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.

Baidu provides an integrated AI ecosystem comprising a cloud‑based AI Open Platform with over 270 pre‑trained model APIs for vision, speech, and language, the DuerOS voice‑assistant SDK for multimodal interaction, and the Apollo autonomous‑driving stack offering perception, planning, and safety‑critical tools. These services run on Baidu’s Kunlun AI chips and the PaddlePaddle deep‑learning framework, delivering scalable, production‑grade performance and pay‑as‑you‑go pricing for developers, enterprise IT teams, and automotive OEMs.

4Paradigm provides an AI enablement platform that delivers industry‑specific large models built from multi‑modal data and a software‑defined compute layer that abstracts hardware for high‑throughput, low‑cost processing. The platform includes AutoML, transfer‑learning tools, and a generative‑AI development suite that automates model creation, code generation, review, and deployment, all delivered via secure, GDPR‑compliant cloud services.

The startup provides a decentralized infrastructure for AI that enables users to access and purchase computing services, data storage, and Model as a Service (MaaS) at scale. This platform addresses the high costs and inefficiencies associated with deploying sophisticated AI solutions, allowing users to optimize performance and resource utilization.

FuriosaAI develops the RNGD data center accelerator, utilizing a Tensor Contraction Processor architecture to enhance the efficiency of AI inference with a power profile of just 150W. This technology enables enterprises to deploy large language models and multimodal applications with low latency and high throughput, significantly reducing energy consumption and operational costs in data centers.

Simplismart provides a high-performance inference engine that enables rapid deployment and fine-tuning of generative AI models on-premises or across various cloud platforms. This technology reduces model deployment time from months to days, significantly lowering operational costs while enhancing inference speed and scalability.

This company offers AI and cloud consulting services, providing businesses with tailored AI solutions for various sectors like automotive, e-commerce, and finance. Their services include data interaction, visualization, AI solutions, and cloud deployment to help businesses innovate and scale.

The startup manufactures a chip that utilizes Compute Express Link technology to enable data center operators to efficiently pool and manage artificial intelligence accelerators, processors, and memory. This approach enhances system performance by providing adequate memory resources for diverse device integration, addressing the challenges of scalability and resource allocation in large-scale computing environments.

The startup operates a high-performance computing data center that provides local companies with essential HPC resources to meet increasing demand. Its platform features an AI marketplace for software developers, integrating a token economy to facilitate transactions and enable profit-sharing for AI application uploads.

Provides a cloud-native developer platform that enables teams to run workloads across AWS, GCP, and Azure with 99.999% uptime and ultra-low latency. It reduces cloud compute costs by 60-80% through AI-driven resource optimization, while offering seamless service integration, military-grade security, and instant compliance across multi-cloud and on-premises environments.

Provides a cloud-agnostic platform, UnifyAI, that streamlines the development and deployment of AI/ML use cases by integrating data pipelines, model training, and monitoring into a single workflow. Reduces time to production by 40% and total cost of ownership by 30%, enabling industries like insurance, banking, and retail to transition from experimentation to scalable, enterprise-grade AI solutions in weeks.

Develops on-device AI semiconductor solutions, including custom NPUs, SoC ASICs, and specialized modules, optimized for low power consumption and high performance in applications like video analytics, security, and robotics. By enabling real-time AI processing with support for multiple models on a single chip, DEEPX addresses the challenges of latency, privacy, and network costs associated with cloud-based systems. Its scalable architecture and 259 patents ensure cost-competitive, silicon-proven products for global markets.

Well-Link provides real-time cloud rendering solutions that enable high-quality, low-latency gaming and digital human experiences across various platforms. Their technology addresses the need for efficient cloud infrastructure, allowing developers to enhance game performance and create immersive metaverse events without the burden of local hardware limitations.

NeuReality designs AI-centric infrastructure that integrates a network addressable processing unit (NAPU) with purpose-built software to streamline AI inference workflows. This solution reduces reliance on traditional CPUs and networking components, addressing the complexity and inefficiencies that hinder AI model deployment and scalability.

NEUCHIPS develops AI ASIC solutions, including the Evo Gen 5 PCIe Card and Gen AI N3000 Accelerator, specifically designed for deep learning inference in data centers. Their technology addresses the need for energy-efficient hardware that minimizes total cost of ownership (TCO) while enhancing performance for machine learning applications.

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.

MetAI generates high-fidelity digital twins and synthetic data to accelerate AI development and validation for industrial applications. Their platform leverages NVIDIA Omniverse and proprietary generative models to rapidly create SimReady environments, enabling faster AI training and simulation.

GENOBOTICS AI provides a cloud‑native platform that applies deep‑learning models to automatically annotate genomic variants and predict their therapeutic relevance. Users upload standard formats (VCF, BAM, FASTQ) and receive batch‑processed results within minutes via RESTful APIs or an interactive dashboard, with end‑to‑end encryption and HIPAA/GDPR compliance for pharmaceutical, biotech, and academic precision‑medicine teams.

Rebellions develops AI accelerators that utilize HBM3e chiplet architecture and 5nm System-on-Chip technology to enhance energy efficiency and computational performance for deep learning applications. The company addresses the need for scalable and efficient AI inference solutions in the rapidly growing generative AI market.

Provides a cloud-agnostic, LLM-agnostic enterprise AI platform that deploys AI agents to automate decision-making, optimize operations, and integrate data across business functions. The platform ensures secure, traceable, and scalable AI adoption, enabling organizations to reduce operational inefficiencies and unlock measurable growth through generative AI use cases.

Speedata develops an Analytics Processing Unit (APU) specifically designed to enhance the performance of big data analytics workloads, achieving up to 100x faster processing and 90% cost savings compared to traditional CPUs and GPUs. This technology addresses the inefficiencies of conventional data processing, enabling enterprises to maximize their data utilization and accelerate time to insight.

Moreh provides a full-stack AI infrastructure platform that integrates PyTorch with GPU virtualization to facilitate the scaling of large language models and AI applications. The platform addresses the challenge of accessibility and resource allocation in hyperscale AI environments, enabling efficient fine-tuning and deployment across multiple GPUs.

The startup offers a cloud-based platform that enables enterprises to create customized artificial intelligence applications by managing diverse data types, including text, images, and audio. This platform automates dataset management, model tracking, deployment, and monitoring, allowing engineers to develop deep learning models more efficiently.

The startup develops AI technology that integrates Microcontroller Units, Central Processing Units, and application processors to enable efficient AI deployment in smart sensors, wearable devices, and robotics. This technology allows clients to transition from costly GPU instances, significantly reducing model size, inference time, and operational costs.

ZETIC.ai provides NPU-powered on-device AI solutions that eliminate the need for cloud servers, significantly reducing operational costs by up to 99%. Their automated pipeline enables rapid transformation of AI models, achieving runtime performance up to 60 times faster than traditional CPU methods within 24 hours.

This startup provides a Kubernetes-native storage solution specifically designed for AI workloads, utilizing a new architecture that combines NVMe and object storage to enhance data management and distribution. It addresses the bottleneck in AI performance by offering storage that is eight times faster and 3.5 times cheaper than existing cloud block storage options, without requiring changes to existing infrastructure.

Granulate provides an AI‑driven platform that continuously profiles cloud‑native workloads and applies kernel‑level tuning—such as CPU frequency scaling, thread affinity, and memory placement—in real time without code changes. The system integrates with AWS, Azure, GCP and Kubernetes, delivering higher CPU efficiency, lower latency, and measurable cloud‑cost reductions. Users monitor performance and savings through a web console or API for data‑driven capacity planning.

DODIL provides a unified platform that aggregates SOC‑II‑compliant GPU and CPU capacity from a global network of data centers, delivering high‑performance compute for AI workloads at 60‑70 % lower cost than traditional cloud providers. The service offers managed provisioning, monitoring, auto‑scaling, and raw compute spaces through a web portal and API, simplifying resource allocation and compliance for developers and engineering teams.

EDGEMATRIX develops Edge AI Boxes equipped with high-performance GPUs for real-time video analysis and processing at the edge, enabling efficient deployment of AI applications in smart city environments. Their technology enhances safety and operational efficiency by detecting anomalies and events through integrated camera systems, facilitating remote management and maintenance.

This company offers an AI cloud platform with low-code/no-code interfaces for building high-performance models. They also provide data science training and consulting services, enabling businesses in Thailand to leverage AI technology.

VirtAI Tech provides GPU pooling and virtualization software that enables unified management and dynamic allocation of GPU resources across multiple servers. This technology enhances GPU utilization and significantly reduces hardware costs for AI application development and training.

Cambricon designs and develops artificial intelligence (AI) processors and acceleration cards for cloud, edge, and terminal applications. Their products, including MLUs and IP cores, are built on advanced architectures to enhance AI computing performance. The company also provides software development platforms and systems to support AI deployment.

Morgenrot provides an API‑first GPU cloud marketplace that aggregates idle GPU capacity from multiple data centers into on‑demand compute, with per‑minute billing and automated workload matching. Its GUI‑driven job manager and TailorNode virtualization layer enable real‑time monitoring, fine‑grained GPU slicing, and multi‑tenant GPU‑as‑a‑Service across on‑premise and public‑cloud environments. The platform helps AI data‑center operators and enterprise R&D teams scale GPU resources instantly while improving utilization and controlling costs.

ECOBLOX provides turnkey AI and HPC data center solutions, offering modular data centers and as-a-service options for rapid deployment of specialized compute infrastructure. They enable organizations to scale AI and HPC capabilities quickly with optimized designs for power, cooling, and density.

OpenPie provides a cloud-native data computing system that integrates structured and unstructured data resources, enabling efficient multi-engine data processing for large AI models. The platform addresses data silos in enterprises by facilitating seamless access and computation across diverse data formats, enhancing the performance of machine learning applications.

The startup develops a family of processors optimized for cloud and edge computing, specifically targeting artificial intelligence and machine learning workloads. Their patent-pending chip design technology reduces transistor count while enhancing performance, enabling businesses to lower power consumption and improve yield and reliability.

The startup operates a customized medical AI platform that provides cloud-based AI medical diagnosis assistance and PACS services, enabling healthcare professionals to access diagnostic support without altering their existing workflows. This platform addresses the need for accessible and efficient medical diagnosis tools, allowing for timely decision-making in various clinical environments.

Moffett AI designs AI chips that accelerate processing in both terminal and cloud environments, enhancing computational efficiency for AI applications. Their technology addresses the demand for faster and more efficient AI processing capabilities in various industries.

Colossal-AI offers a cloud-based platform that accelerates deep learning model training and inference by up to 10 times while reducing development costs by 100 times. This solution enables organizations to efficiently scale AI capabilities from single GPU setups to large distributed clusters, addressing the high computational demands and expenses associated with large model development.

Anyon Technologies offers a quantum supercomputing platform that integrates proprietary QPUs with NVIDIA GPU acceleration. This hybrid approach enables enterprises to develop and deploy quantum-enhanced applications for AI, finance, and scientific research, bridging classical and quantum computing workflows.