Top 50 Ai Compute Platform - Series B

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.

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.

TensorWave provides a cloud platform optimized for AI workloads, utilizing AMD's Instinct MI300X accelerators for enhanced training, fine-tuning, and inference capabilities. The platform offers immediate availability, lower total cost of ownership, and seamless integration with popular frameworks like PyTorch and TensorFlow, addressing the need for efficient and scalable AI compute solutions.

The startup provides CPU cores designed for managing circuits and complex data center processors, focusing on flexibility and performance. Their technology enhances AI integration, enabling clients to efficiently deploy processors across diverse computing systems.

Io.net Cloud offers a decentralized computing network that provides machine learning engineers with instant, permissionless access to global GPU resources for their workloads. This platform enables efficient deployment of pre-configured clusters, significantly reducing costs and deployment time for AI startups.

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.

Union.ai is a Kubernetes-native workflow orchestration platform that streamlines the development, management, and deployment of AI models at scale. It addresses challenges such as disconnected development teams, high cloud costs, and slow time-to-market by providing a unified interface for automating workflows and optimizing resource usage.

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.

Ampere Computing designs high-performance data center processors, including the Ampere Altra and AmpereOne families, which feature up to 192 cores optimized for cloud-native applications. These processors deliver significant price-performance advantages, achieving up to 50% better efficiency compared to traditional x86 architectures, while addressing the growing demand for scalable and sustainable computing 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.

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.

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.

RunPod is a cloud platform that provides globally distributed GPU resources for deploying and scaling machine learning applications, enabling developers to run AI workloads without managing infrastructure. The platform reduces cold-start times to under 250 milliseconds and offers flexible pricing, allowing users to efficiently handle fluctuating demand while minimizing operational costs.

EnCharge AI develops a scalable analog in-memory computing platform that enhances AI performance by achieving 20 times higher efficiency and 10 times lower total cost of ownership compared to traditional GPU solutions. This technology enables on-device processing, significantly reducing CO2 emissions and ensuring data privacy while making advanced AI accessible beyond cloud infrastructure.

Taalas provides a platform that transforms any AI model into custom silicon, creating Hardcore Models that are hardwired for optimal performance. This approach significantly enhances computational efficiency, achieving up to 1000 times the performance of traditional software implementations.

The startup provides a hybrid cloud platform that combines GPU cloud services with AI controller software for on-premises deployments, enabling enterprises to efficiently manage AI workloads. This solution allows businesses to optimize their data center resources while seamlessly scaling to the public cloud as needed.

Modular provides a unified AI software development platform that enables developers to optimize and deploy AI models across various cloud environments without rewriting code. By leveraging high-performance Mojo programming and a secure inference stack, the platform significantly reduces cloud costs while enhancing computational efficiency for enterprise applications.

Luminous Computing develops photonics chips designed to provide the necessary compute, memory, and bandwidth for advanced artificial intelligence applications. This technology addresses the limitations of current hardware, enabling instant processing of complex queries and facilitating the development of next-generation AI solutions.

Clarifai offers an end-to-end AI lifecycle platform that automates data labeling, model training, and deployment, enabling organizations to build and operationalize AI applications efficiently. By standardizing workflows and optimizing compute resources, the platform reduces development time and costs, allowing enterprises to scale AI solutions rapidly.

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.

Rain AI manufactures energy-efficient hardware specifically designed for artificial intelligence applications, utilizing advanced power management techniques to minimize energy consumption. This technology addresses the high energy demands of AI processing, enabling organizations to reduce operational costs and environmental impact.

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.

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.

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.

Untether AI develops high-density AI accelerators that utilize at-memory computing to enhance the speed and energy efficiency of AI inference tasks. Their technology enables real-world applications, such as autonomous vehicles and smart cities, to operate more effectively and affordably.

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.

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.

Develops a proprietary AI supercomputing architecture using a 3nm process and multi-threaded processing (ThreadArch®) to deliver energy-efficient, high-performance training for large AI models. This technology addresses the growing demands of AI applications and drug discovery by reducing compute costs and energy consumption while enabling faster model training and in silico drug design.

The startup offers a composable AI cloud platform that enables eCommerce teams to create, configure, and deploy tailored AI solutions without requiring prior AI expertise. This platform addresses the challenge of outdated technology in eCommerce by providing a user-friendly toolkit that allows brands to rapidly implement AI to enhance conversion rates and optimize margins.

Outerbounds provides a human-centric infrastructure for machine learning and data science, utilizing the open-source Metaflow framework originally developed at Netflix. The platform enables data scientists and ML engineers to efficiently develop, scale, and deploy production-ready AI systems while minimizing infrastructure management burdens.

The startup develops intelligent information processing products that optimize data center operations by enhancing performance and power efficiency while minimizing capital expenditures on computing and storage hardware. Their technology specifically addresses the high costs and energy consumption associated with big data analytics, deep learning, and large-scale computing 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.

D-Matrix has developed Corsair, an AI inference platform that achieves 60,000 tokens per second with 1 ms latency for Llama3 8B models, significantly enhancing throughput and energy efficiency in datacenters. This technology addresses the high computational costs and energy consumption associated with large-scale AI inference, enabling organizations to scale their AI capabilities sustainably.

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.

Celestial AI develops the Photonic Fabric™, an optical interconnect technology that enhances data transfer speeds and reduces latency in AI computing and memory infrastructure. This technology addresses the limitations of traditional interconnects by enabling high-bandwidth, low-latency connections essential for processing extensive AI workloads efficiently.

SynthBee is developing a computing intelligence platform that utilizes advanced algorithms to enhance data processing and decision-making capabilities. This technology enables organizations to increase innovation efficiency while ensuring data security and scalability in their operations.

The startup operates a data intelligence platform that utilizes machine learning for automated performance and fault modeling in microservices and serverless computing architectures. This technology enables developers to minimize manual errors and enhance software delivery efficiency.

The startup offers a machine learning infrastructure platform that provides a flexible operating system and virtualization interface for building and deploying machine learning and deep learning applications at scale. This technology enables enterprises to manage applications and hardware from a single terminal, resulting in increased productivity, reduced operational costs, and faster delivery times.

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.

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.

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.

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.

The startup operates an AI-driven venture studio that automates complex, multi-level decision-making processes traditionally reliant on human input. By providing a shared technological and operational framework, the company enables teams of entrepreneurs and engineers to efficiently transform leading research into market-ready products while offering seed financing and access to industry networks.

The startup offers an AI-enabled life sciences research and development platform that manages controlled-access multimodal data at an enterprise scale. This platform enhances data analysis and automates workflows, enabling researchers to conduct high-impact studies and collaborations with reduced resource requirements.

Recogni develops a multimodal AI inference system utilizing its proprietary Pareto AI Math to enhance performance while significantly reducing power consumption. This technology addresses the high costs and energy demands of generative AI models, enabling efficient and accurate processing for data centers.

Eta Compute develops a no-code MLOps toolchain that optimizes machine learning models for low-power edge devices, enhancing their efficiency and accuracy. This technology enables enterprises to effectively monitor resources while minimizing energy consumption and inference time.

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.

Axelera AI develops and sells high-performance, energy-efficient AI inference hardware for edge devices. Their Metis AI Platform integrates a specialized in-memory computing architecture with a comprehensive software stack, enabling efficient deployment of deep learning models for computer vision and natural language processing applications.

Oort is a decentralized cloud computing platform that utilizes a blockchain-based verification layer to integrate global resources for secure data storage and AI model training. By leveraging idle computing power from data centers and edge devices, Oort reduces costs by up to 80% while ensuring 100% privacy for all data services.

Esperanto Technologies develops massively parallel, energy-efficient chips based on the RISC-V instruction set architecture, specifically designed for Generative AI and high-performance computing (HPC) applications. Their ET-SoC-1 chip features over a thousand low-power RISC-V cores, providing superior compute efficiency and significantly reducing total cost of ownership for AI inference and HPC workloads.