Top 50 Ai Compute Platform in Asia

Creative Humans AI provides AwasmCloud, a supercomputing platform that abstracts infrastructure to deliver on‑demand, low‑latency compute for real‑time AI and AGI workloads. The service automates deployment, scaling, and runtime orchestration, allowing engineers and enterprises to focus on model development rather than resource management.

The startup develops an AI-driven decentralized computing platform that integrates blockchain technology with edge computing protocols, enabling users to share computing resources and process data efficiently. By utilizing smart contracts and decentralized storage, the platform enhances transaction verification and provides reliable computing power for decentralized applications (dApps).

Exabits specializes in refining raw GPU assets from leading manufacturers to support advanced AI infrastructure. The company provides access to high-demand hardware, including GB200s, H100s, H200s, and RTX5090s. This focus ensures that organizations have the necessary compute power for demanding AI workloads and innovation.

This company offers a cloud platform optimized for the entire AI lifecycle, from model development and training to deployment. Their AI supercloud provides scalable computing resources, workflow automation, and performance monitoring to accelerate AI innovation for businesses.

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.

Gongji Technology is a platform that utilizes distributed computing power and advanced scheduling algorithms to optimize resource allocation for AI applications. It provides flexible, on-demand access to high-performance GPU resources, enabling developers to efficiently manage costs and scale their computing needs without the complexities of server management.

Run:ai is an AI cluster management platform that optimizes GPU resource utilization through dynamic scheduling, GPU fractioning, and workload management. It enables organizations to increase AI workload efficiency by up to 10 times, accelerating development and reducing operational 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.

Edge Matrix Computing operates a decentralized AI infrastructure that utilizes a GPU-based distributed computing network to provide scalable computing resources for AI applications. This platform addresses the inefficiencies of centralized computing by enabling developers to deploy and manage AI dApps with reduced latency and improved resource allocation.

The startup offers a cloud-based platform that optimizes AI infrastructure and workloads through the integration of open-source tools and resource estimation. This platform enhances operational efficiency by improving the deployment process of artificial intelligence applications for businesses.

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.

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.

PinGo utilizes AI, decentralized physical infrastructure networks (DePIN), and GPU technology to enable users to monetize idle GPU resources for AI model training. This platform addresses the high costs and accessibility issues associated with computational power, allowing users to contribute to and benefit from a global network of GPU resources.

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.

Kluisz provides an AI-native cloud infrastructure platform designed for the AI era, aiming to reduce operational overhead and accelerate deployments. The platform autonomously builds, optimizes, and manages infrastructure while enforcing Zero Trust security protocols. It offers unified cloud services engineered to handle complex AI workloads without the typical hyperscaler complexity.

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.

Luchen Technology provides a platform for training and deploying large AI models, significantly reducing training and inference costs by up to 90% while enhancing model capacity and speed. Their solutions enable businesses to efficiently build high-quality AI applications with minimal resources, streamlining the development process across various hardware environments.

Exlords develops an AI and machine learning-integrated hardware platform for edge processors, enabling real-time AI inference on user devices through on-device neural processing units. This technology addresses the need for efficient, localized computing solutions that enhance performance and connectivity in heterogeneous computing environments.

Zhihe Computing develops high-performance, scalable AI computing CPUs based on the RISC-V architecture, enabling efficient processing for AI applications. The company addresses the need for advanced computational power in AI scenarios, leveraging a team with extensive industry experience to enhance performance and scalability.

Axonvertex AI offers a private, on‑premise AI engineering platform for healthcare and clinical‑trial organizations, enabling secure execution of large language and tiny models with built‑in NIST‑aligned responsible‑AI safeguards, human‑in‑the‑loop controls, and FHIR‑based data integration. The platform provides auditable AI agents and edge inference to support compliant decision‑support and workflow automation while keeping patient data local.

Rutilea provides an AI development platform and GPU cloud services that facilitate the selection, development, and deployment of tailored AI models in a secure environment. The platform enhances operational efficiency by enabling businesses to implement AI solutions that streamline processes and improve decision-making.

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.

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.

Provides an AI-driven platform for optimizing deep learning models through techniques like model compression, pruning, and heterogeneous scaling. This enables businesses to reduce GPU computing costs by up to 80%, achieve up to 6x faster inference speeds, and deploy resource-efficient models on edge devices while ensuring data privacy and security.

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.

Shansi Kaiwu provides a GPU Cloud platform built on heterogeneous multi-core architecture, specifically designed for high-performance computing (HPC) and artificial intelligence (AI) applications. The company offers scalable infrastructure that supports complex computational tasks, enabling efficient model training, fine-tuning, and inference across various industries.

Cortica provides an autonomous AI platform that converts visual, audio, radar and time‑series sensor streams into compressed neural signatures using self‑learning, brain‑inspired networks. The system trains on unlabelled production data, runs inference on low‑power hardware, and adapts continuously to avoid bias, allowing partners in manufacturing, automotive, security, and healthcare to deploy domain‑specific perception and analytics without building foundational models.

The startup provides AI hardware-software solutions that enhance computational efficiency and performance for data-intensive applications. By integrating specialized AI accelerators with optimized software frameworks, the company addresses the high energy consumption and latency issues faced by traditional computing systems.

Avalanc offers a low-code AI platform designed to streamline deep learning development, enabling users to create and deploy models with minimal coding effort. This tool addresses the complexity and time constraints associated with traditional deep learning workflows, allowing teams to accelerate their projects significantly.

Myelin Foundry develops edge AI algorithms that process complex unstructured data from video, voice, and sensors in real-time, optimizing performance on low-power devices. This technology enables enterprises to achieve immediate insights and automation, reducing operational costs and enhancing user experiences.

Qingwei Intelligent Technology designs and manufactures AI chips optimized for high-performance computing tasks in artificial intelligence applications. Their technology addresses the demand for efficient processing power in AI systems, enabling faster data analysis and improved machine learning capabilities.

ProjectX offers Infinity, a cloud-native operating system delivered entirely through the browser for high-performance computing. This platform enables users to run GPU-accelerated applications like Blender and VS Code instantly without downloads or virtual machines. Infinity abstracts compute resources, providing elastic GPU scaling and persistent environments for AI workflows, design, and development across any device.

This company provides a Vision AI Engine specifically designed for automated cosmetic defect detection on automotive production lines. The system integrates without requiring line redesign, enabling real-time, sub-millimeter accurate inspection at every manufacturing stage. They also offer an end-to-end AI development platform for building and deploying intelligent industrial applications.

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.

HyperAccel engineers specialized AI semiconductors, utilizing a novel LPU architecture designed for high-performance and energy-efficient Generative AI workloads. Their product line spans from edge devices to cloud datacenters, offering optimized solutions for LLM inference. The company supports leading AI frameworks through a dedicated software platform, ensuring seamless integration for developers.

Ripple Software provides liquid-cooled computing power infrastructure designed for artificial intelligence-generated content (AIGC) applications. This technology enhances thermal efficiency and performance, addressing the high energy consumption and heat management challenges faced by data centers.

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.

Optical Standard Technology designs and manufactures silicon photonic computing chips and boards that utilize phase-change materials to enhance computational density and precision for AI applications. This technology significantly reduces deployment and operational costs in high-demand scenarios such as autonomous driving and large-scale data processing.

Zesty is a cloud infrastructure optimization platform that utilizes machine learning automation to provide real-time recommendations for resource allocation, significantly reducing cloud costs and waste. By automatically identifying underutilized resources and adapting to demand fluctuations, Zesty ensures organizations only pay for their actual usage while maximizing operational efficiency.

Zhonghao Xinying develops specialized AI chips focused on enhancing computational efficiency for artificial intelligence applications. Their technology addresses the high energy consumption and processing limitations faced by current AI systems, enabling faster and more cost-effective AI deployment.

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.

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.

Ocean provides a decentralized protocol for tokenizing and monetizing AI models and data while maintaining user privacy. The platform utilizes Data NFTs and Datatokens to control access and enable secure Compute-to-Data functionality. This infrastructure allows developers to build and train AI capabilities efficiently on secure, modular computational resources.

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.

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.

Intel® Tiber™ AI Studio is a machine learning operating system that enables data scientists to build, deploy, and manage AI applications across hybrid and multi-cloud environments using Kubernetes orchestration. It streamlines the machine learning workflow by unifying code, models, and compute resources, allowing for faster experimentation and improved resource utilization.

ThinkForce is a Shanghai-based manufacturer of artificial intelligence chips designed to enhance processing efficiency in machine learning applications. Their technology addresses the demand for high-performance computing solutions in industries reliant on real-time data analysis.

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.

Provides a serverless AI platform that generates realistic videos from text and understands video content through multimodal AI models. By leveraging sparse training techniques, the platform delivers real-time results with reduced compute requirements, enabling efficient deployment via APIs, public cloud, or on-premise installations.

ClearML is an open-source MLOps platform that automates the machine learning lifecycle, enabling developers to build, train, and deploy AI models at scale with minimal code. It addresses the challenges of fragmented workflows and inefficient resource utilization by providing a unified solution for managing AI infrastructure across on-premise, cloud, and hybrid environments.