Top 50 Ai Compute Platform in Asia

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.

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.

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.

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.

Krutrim provides an AI computing infrastructure and AI-powered applications tailored for the Indian market, enabling businesses to leverage machine learning and data analytics. This platform addresses the need for accessible and scalable AI solutions, enhancing operational efficiency and decision-making capabilities for local enterprises.

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.

EdgeCortix develops the SAKURA-II Edge AI Platform, an energy-efficient AI accelerator that delivers up to 240 TOPS for real-time inferencing in compact, low-power modules. This technology addresses the need for high-performance AI processing at the edge, significantly reducing operational costs across various sectors, including defense, robotics, and smart manufacturing.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Cloudwalk offers an AIoT platform that integrates edge devices, a collaborative operating system (CWOS), and multimodal foundation models to provide on‑device inference and standardized APIs for vision, speech, and language processing. The solution includes privacy‑computing, data‑governance, and AI‑Agent tools, allowing large enterprises and public agencies in finance, manufacturing, energy, and smart city domains to deploy AI capabilities without extensive custom integration.

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.

VESSL AI offers an end-to-end MLOps platform that enables machine learning teams to build, train, and deploy models efficiently across various infrastructures with a single command. The platform addresses the challenges of resource management and deployment speed by providing serverless deployment, real-time monitoring, and automated CI/CD workflows.

TakeMe2Space is developing AI-powered satellites in low Earth orbit that provide affordable access to orbital infrastructure for students, researchers, and enterprises. Their technology enables users to run compute tasks in space, upload AI models for earth observation, and capture high-resolution images, addressing the high costs and long wait times traditionally associated with satellite access.

Neurolov offers a cloud‑native platform that runs curated machine‑learning models on genomics, transcriptomics, proteomics and phenotypic data to produce predictive biomarkers, pathway activity scores, and compound efficacy forecasts. The service automates data ingestion, preprocessing, feature engineering and model inference, delivering results through interactive dashboards and API endpoints for integration with LIMS and downstream analysis. By providing a managed, scalable compute environment with versioned model registries and GxP/HIPAA compliance, it shortens the turnaround time for drug discovery teams.

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.

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 customizable AI platform for enterprises to build and integrate AI agents, automate workflows, and analyze voice data across cloud and on-premise environments. It streamlines processes like data enrichment, compliance monitoring, and inter-departmental collaboration, enabling organizations to improve efficiency and leverage AI-driven insights tailored to their specific needs.

Pints.ai provides a secure, on-premise AI platform for financial institutions to leverage generative AI for research and analytics. Our solution enables advanced capabilities like private LLMs and predictive modeling while ensuring strict data privacy and regulatory compliance.

Hailo develops AI processors optimized for deep learning applications on edge devices, enabling high-performance video processing and analytics with low power consumption. Their technology addresses the need for efficient AI inferencing in various industries, including automotive and industrial automation, by facilitating the deployment of complex neural networks in resource-constrained environments.

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 develops an AI-based NeuroMosAIc Processor (NMP) that integrates a RISC-V architecture for high-performance computing in semiconductor applications. Its technology enables clients to efficiently evaluate neural network performance metrics such as accuracy, memory bandwidth, and run-time using SDK solutions compatible with TensorFlow, Caffe, PyTorch, and ONNX frameworks.

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.

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.

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.

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.

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.

Ostrich AI provides a secure platform for enterprise AI and machine learning projects, utilizing privacy-preserving machine learning and a secured data-sharing protocol to ensure compliance and data integrity. The platform enables organizations to efficiently deploy AI models at a significantly reduced cost, achieving up to 60% savings on cloud infrastructure while facilitating collaboration among data scientists.

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.

InfuseAI offers the PrimeHub AI platform, which streamlines machine learning workflows by integrating essential MLOps tools into a single environment, enabling teams to develop, train, and deploy AI models significantly faster. This platform addresses the inefficiencies of managing multiple DevOps tools, allowing organizations to enhance productivity and reduce the time required to bring AI solutions online.

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.

Mobilint develops neural processing unit (NPU) solutions optimized for edge AI applications, achieving up to 80 TOPS performance with low power consumption. Their technology supports over 100 AI algorithm models and provides a user-friendly SDK, enabling efficient development for various edge devices.

Nota AI develops NetsPresso, a hardware-aware AI optimization platform that streamlines the deployment of AI models across various devices. This technology enables efficient on-device AI solutions, reducing computational costs and enhancing performance for industries such as healthcare, automotive, and transportation.

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.

The startup develops an artificial intelligence platform that utilizes patented miniaturization technology to optimize computation and customize large language model (LLM) training. This approach addresses the high costs and accuracy issues organizations face when deploying AI solutions.