Vapor IO: Veea Inc. and Vapor IO Announce a Strategic Partnership to Provide Turnkey AI-as-a-Service Pioneering Solutions

Veea Inc. and Vapor IO announced a partnership to offer turnkey AI-as-a-Service (AIaaS) to enterprises, municipalities and others without investing in capital-intensive edge devices, servers, networking equipment and data center facilities. For enterprise applications, such as Smart Manufacturing, Smart Warehouses, Smart Hospitals, Smart Schools, Smart Construction, Smart Infrastructure, and many others, Veea Edge Platform? collects and processes the raw data at the Device Edge, where user devices, sensors and machines connect to the network, most importantly, for reasons of low-latency, data privacy and data sovereignty. VeeaWare® full stack software running on VeeaHub® devices and on third-party hardware solutions with GPUs, TPUs or NPUs, such as NVIDIA AGX Orin and Qualcomm Edge AI Box-based hardware on a Veea computing mesh, provide for the full gamut of AI inferencing with cloud-native edge applications and AI-driven cybersecurity with bespoked Agentic AI and AIoT for the specific use cases. Combined with its VeeaCloud management functions, AIoT platform and extension of network slicing through the LAN with SDN and NFV, Veea Edge Platform offers an unrivaled capability for AI inferencing for enterprise use cases at the edge. The core of Vapor IO?s Zero Gap AI is built around Supermicro MGX servers with the NVIDIA GH200 Grace Hopper Superchip for high-performance accelerated computing and AI applications. The Zero Gap AI makes it possible to simultaneously deliver AI inferencing and train complex models while supporting 5G private networks, including NVIDIA Aerial-based 5G private network services. Through a PoC together with Supermicro and NVIDIA in Las Vegas, Vapor IO demonstrated how Zero Gap AI customers can receive the benefits of AI inferencing for a range of use cases including by those in mobile environments with the highest level of performance and reliability that may be achieved today. For low-latency use cases, Zero Gap AI is offered as high-performance micro data centers, strategically placed in close proximity where AI inferencing is delivered. Zero Gap AI offering provides for the AI tools, libraries, SDKs, pre-trained models, frameworks and other components that may optionally be employed to develop AI apps. The combined capabilities of Veea Edge Platform and Zero Gap AI, offer a unified, automated platform with orchestration for seamless workload distribution, which enables a new class of collaborative, distributed AI applications as an AI-in-a-Box solution: VeeaCloud management of GPU clusters - Plays a crucial role in balancing performance, scalability, and efficiency for AI inferencing, while utilizing cloud orchestration for resource optimization, model updates, and intelligent workload distribution. Providing On-Demand AI Compute ? Eliminates the need for enterprises to invest in costly on-prem AI hardware by offering scalable, GPU-accelerated AI compute at the edge. Enabling AI at Any Scale ? Supports AI workloads ranging from lightweight IoT analytics to full-scale deep learning training, ensuring enterprises can adopt AI incrementally or at full scale. Harnessing Agentic AI ? Integrates intelligent, autonomous decision-making capabilities that enable AI systems to adapt and optimize their performance in real-time, enhancing the effectiveness of applications across various edge environments. Facilitating Federated Learning ? Supports collaborative model training across distributed edge devices while maintaining data privacy, allowing enterprises to leverage insights from decentralized data sources without compromising sensitive information. Supporting Model Hosting & AI Inference ? Allows users to deploy, manage, and scale AI models in real-time, with low-latency inference APIs available across edge locations. Offering Bare Metal and Virtualized AI Instances ? Users can lease dedicated AI hardware or deploy workloads in multi-tenant GPU/CPU environments, ensuring flexibility for both small and large-scale AI applications. Integrating Edge Storage & AI Data Management ? Includes NVMe-based high-speed caching for inference and object storage for large-scale AI datasets, reducing reliance on cloud-based data transfers. Ensuring Seamless Connectivity Options ? A range of ultra-low latency connectivity options to optimize AI data transfer between on-prem devices and Edge-to-Edge compute. Reducing AI Deployment Complexity ? Automates AI workload orchestration, allowing businesses to expand, migrate, or failover AI models across distributed edge nodes without manual reconfiguration. Accelerating Time-to-Value for AI Deployments ? Provides a pre-integrated solution that reduces AI setup time from months to minutes, allowing enterprises to launch AI-powered solutions with minimal friction and on-going maintenance. Veea Inc. and Vapor IO announced a partnership to offer turnkey AI-as-a-Service (AIaaS) to enterprises, municipalities and others without investing in capital-intensive edge devices, servers, networking equipment and data center facilities. For enterprise applications, such as Smart Manufacturing, Smart Warehouses, Smart Hospitals, Smart Schools, Smart Construction, Smart Infrastructure, and many others, Veea Edge Platform? collects and processes the raw data at the Device Edge, where user devices, sensors and machines connect to the network, most importantly, for reasons of low-latency, data privacy and data sovereignty. VeeaWare® full stack software running on VeeaHub® devices and on third-party hardware solutions with GPUs, TPUs or NPUs, such as NVIDIA AGX Orin and Qualcomm Edge AI Box-based hardware on a Veea computing mesh, provide for the full gamut of AI inferencing with cloud-native edge applications and AI-driven cybersecurity with bespoked Agentic AI and AIoT for the specific use cases. Combined with its VeeaCloud management functions, AIoT platform and extension of network slicing through the LAN with SDN and NFV, Veea Edge Platform offers an unrivaled capability for AI inferencing for enterprise use cases at the edge. The core of Vapor IO?s Zero Gap AI is built around Supermicro MGX servers with the NVIDIA GH200 Grace Hopper Superchip for high-performance accelerated computing and AI applications. The Zero Gap AI makes it possible to simultaneously deliver AI inferencing and train complex models while supporting 5G private networks, including NVIDIA Aerial-based 5G private network services. Through a PoC together with Supermicro and NVIDIA in Las Vegas, Vapor IO demonstrated how Zero Gap AI customers can receive the benefits of AI inferencing for a range of use cases including by those in mobile environments with the highest level of performance and reliability that may be achieved today. For low-latency use cases, Zero Gap AI is offered as high-performance micro data centers, strategically placed in close proximity where AI inferencing is delivered. Zero Gap AI offering provides for the AI tools, libraries, SDKs, pre-trained models, frameworks and other components that may optionally be employed to develop AI apps. The combined capabilities of Veea Edge Platform and Zero Gap AI, offer a unified, automated platform with orchestration for seamless workload distribution, which enables a new class of collaborative, distributed AI applications as an AI-in-a-Box solution: VeeaCloud management of GPU clusters - Plays a crucial role in balancing performance, scalability, and efficiency for AI inferencing, while utilizing cloud orchestration for resource optimization, model updates, and intelligent workload distribution. Providing On-Demand AI Compute ? Eliminates the need for enterprises to invest in costly on-prem AI hardware by offering scalable, GPU-accelerated AI compute at the edge. Enabling AI at Any Scale ? Supports AI workloads ranging from lightweight IoT analytics to full-scale deep learning training, ensuring enterprises can adopt AI incrementally or at full scale. Harnessing Agentic AI ? Integrates intelligent, autonomous decision-making capabilities that enable AI systems to adapt and optimize their performance in real-time, enhancing the effectiveness of applications across various edge environments. Facilitating Federated Learning ? Supports collaborative model training across distributed edge devices while maintaining data privacy, allowing enterprises to leverage insights from decentralized data sources without compromising sensitive information. Supporting Model Hosting & AI Inference ? Allows users to deploy, manage, and scale AI models in real-time, with low-latency inference APIs available across edge locations. Offering Bare Metal and Virtualized AI Instances ? Users can lease dedicated AI hardware or deploy workloads in multi-tenant GPU/CPU environments, ensuring flexibility for both small and large-scale AI applications. Integrating Edge Storage & AI Data Management ? Includes NVMe-based high-speed caching for inference and object storage for large-scale AI datasets, reducing reliance on cloud-based data transfers. Ensuring Seamless Connectivity Options ? A range of ultra-low latency connectivity options to optimize AI data transfer between on-prem devices and Edge-to-Edge compute. Reducing AI Deployment Complexity ? Automates AI workload orchestration, allowing businesses to expand, migrate, or failover AI models across distributed edge nodes without manual reconfiguration. Accelerating Time-to-Value for AI Deployments ? Provides a pre-integrated solution that reduces AI setup time from months to minutes, allowing enterprises to launch AI-powered solutions with minimal friction and on-going maintenance.