Top 50 Ai Compute Platform - Late Stage

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.

CoreWeave provides a Kubernetes-native cloud platform optimized for large-scale, GPU-accelerated workloads, offering on-demand access to a diverse range of NVIDIA GPUs and CPU instances. This infrastructure enables businesses to achieve up to 35 times faster performance and 80% cost savings compared to traditional cloud providers, addressing the need for efficient and scalable compute resources in AI and machine learning applications.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Develops photonic computing solutions that integrate optical and electronic components to accelerate AI workloads, addressing the limitations of traditional electronic systems, such as the "memory wall." Products like the HUMMINGBIRD optical network-on-chip and PACE photonic arithmetic engine enable exponential increases in processing speed and efficiency for domain-specific applications.

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.

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.

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.

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.

The startup operates a cloud computing edge platform that enables the deployment and management of virtual machines, server-less applications, and enterprise website security, including DDoS protection. This platform addresses the challenges of workload acceleration and infrastructure management, allowing developers and enterprises to enhance their operational efficiency and security.

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.

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.

DreamBig Semiconductor offers a Chiplet platform with SMARTNIC-DPU solutions designed for low latency and high throughput in AI, data centers, and storage acceleration. Their technology addresses the need for efficient data processing and inherent security in high-demand computing environments.

Liquid AI develops non-transformer generative AI models, known as Liquid Foundation Models (LFMs), which require less memory while maintaining high performance across various applications. These models address the inefficiencies and high computational costs associated with traditional large language models, enabling scalable AI solutions for diverse industries.

Ventana develops high-performance RISC-V CPUs with extensible instruction sets, available as multi-core chiplets or core IP, tailored for demanding applications in cloud, data center, 5G, AI, and automotive sectors. Their technology enables rapid productization and cost-effective custom system designs, addressing the need for efficient and scalable processing solutions in modern computing environments.

SiMa.ai develops a software-centric platform utilizing its proprietary Machine Learning System on Chip (MLSoC) technology to enable efficient deployment of multimodal AI applications at the edge. This platform addresses the need for high-performance, power-efficient solutions that can scale across various edge devices and applications, significantly improving processing speed and energy consumption.

SiPearl is developing a high-performance, low-power microprocessor specifically for supercomputing and artificial intelligence, designed to integrate with any third-party accelerator. This technology addresses the need for efficient processing of large volumes of data in critical fields such as medical research, energy management, and climate modeling, while minimizing carbon footprint.

Together AI provides a cloud-based platform for training, fine-tuning, and deploying open-source generative AI models using NVIDIA's latest GPUs, enabling users to achieve high performance at a lower cost. The platform addresses the need for scalable AI infrastructure by offering customizable solutions that support the entire generative AI lifecycle, from model development to production deployment.

Gensyn is a machine learning compute protocol that connects distributed resources to facilitate the training of deep learning models. This approach addresses the need for open, permissionless, and neutral frameworks that enable efficient scaling and collaboration in machine intelligence development.

Axelera AI manufactures AI acceleration hardware, specifically the Metis AI Processing Unit (AIPU), designed for efficient edge computing with up to 214 TOPS performance and 15 TOPS per watt. The technology addresses the need for cost-effective and energy-efficient solutions in generative AI and computer vision applications across various industries, including retail and security.

Abacus.AI offers an AI-assisted data science and MLOps platform that enables enterprises to automate the development and deployment of machine learning models using state-of-the-art generative AI technology. The platform addresses the complexity of building and managing applied AI systems by allowing organizations to create custom AI agents and streamline workflows across various data sources.

WEKA provides a cloud-native, software-defined data platform that enables organizations to efficiently store, process, and manage large volumes of data across on-premises and cloud environments. By transforming stagnant data silos into streaming data pipelines, WEKA enhances performance for AI and high-performance computing workloads while reducing energy consumption and carbon emissions.

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.

webAI provides an enterprise platform for on-device AI development and deployment, enabling organizations to build, train, and run custom models locally. This approach ensures full data sovereignty, reduces latency, and lowers operational costs by eliminating cloud dependencies.