Ankündigung • Jul 02
SoftBank Corp. to Report Q1, 2027 Results on Aug 04, 2026 SoftBank Corp. announced that they will report Q1, 2027 results at 3:30 PM, Tokyo Standard Time on Aug 04, 2026 Ankündigung • May 12
SoftBank Corp., Annual General Meeting, Jun 23, 2026 SoftBank Corp., Annual General Meeting, Jun 23, 2026. Ankündigung • Apr 04
SoftBank Corp. to Report Fiscal Year 2026 Results on May 11, 2026 SoftBank Corp. announced that they will report fiscal year 2026 results at 3:30 PM, Tokyo Standard Time on May 11, 2026 Ankündigung • Mar 06
Softbank Corp. Unveils Telco Ai Cloud and Aitras Platform SoftBank Corp. unveiled its strategy for the next-generation of social infrastructure, marking a fundamental evolution in the role of telecommunications carriers. SoftBank announced its transition from a traditional carrier that simply moves data – raw, uninterpreted data packets – to an AI-native infrastructure provider enabling distributed AI workloads across edge and cloud environments. SoftBank's Telco AI Cloud vision evolves the network into a central nervous system: an active computation platform that operates AI models directly within the infrastructure. Through AI-RAN-based MEC (Multi-access Edge Computing), SoftBank can now orchestrate and broker AI workloads across this distributed edge, offloading GPU compute to deliver real-time, reliable inference where it is needed. By embedding this intelligence from the core to the edge, SoftBank is creating a distributed platform that delivers meaning, not just data, enabling immediate decision-making for robotics, autonomous systems, and smart cities. For Physical AI, this means resource-constrained robots can now perform complex, scalable behaviors that would otherwise be difficult to achieve independently; powered not by what they carry but by the network intelligence that surrounds them. A key highlight of the announcement was SoftBank's focus on 'Physical AI': the convergence of AI with the physical world of robotics. Unlike traditional centralized clouds, Telco AI Cloud brings intelligence to the edge, enabling robots to make split-second decisions based on sensor data, performing complex behaviors that their onboard hardware alone could not independently support. Following a collaboration with Yaskawa Electric Corporation focused on deploying robots in real-world environments, SoftBank successfully demonstrated a joint proof-of-concept with Ericsson. The demonstration showcased how AI-RAN networks can optimize connectivity for robots, ensuring the stability required to work safely alongside humans in dynamic environments. In collaboration with Mitsubishi Heavy Industries Ltd., SoftBank is deploying its 'AITRAS' platform in edge data center environments to support industrial use cases. This initiative brings secure AI inference to factory floors, promoting digital transformation in the manufacturing sector and helping to address labor shortages through automation. To foster global innovation, SoftBank has open-sourced the Dynamic Scoring Framework (DSF), a core function of the AITRAS Orchestrator, SoftBank's AI-RAN product. By sharing this technology with the open-source community, SoftBank aims to lower barriers to entry for AI-RAN adoption, inviting developers worldwide to contribute to a more efficient and accessible global infrastructure. SoftBank's Telco AI Cloud vision represents more than a technological upgrade; it is a fundamental shift from transporting data to distributing intelligence. By evolving the network into a ubiquitous AI platform, SoftBank enables the network to not just connect devices but empowers carriers with immediate, context-aware understanding, wherever they operate. Whether alleviating labor shortages through intelligent automation, enhancing industrial safety with edge AI, or enabling robots to perform beyond their own hardware limitations through network-distributed compute, SoftBank is building a central nervous system for society, one where distributed AI infrastructure serves as the catalyst for a more sustainable, productive, and connected future for all. Ankündigung • Feb 27
Northeastern University, SoftBank Corp., Keysight, and zTouch Networks Demonstrate LTM-Powered Autonomous Agentic AI-RAN at MWC Barcelona 2026 Northeastern University's Institute for Intelligent Networked Systems (INSI), SoftBank Corp. (SoftBank), Keysight Technologies, and zTouch Networks are demonstrating Autonomous Agentic AI-RAN (AgentRAN) at Mobile World Congress (MWC) Barcelona 2026. AgentRAN is a first-of-its-kind system that coordinates a hierarchy of AI agents to translate high-level operator intents into real-time, autonomous 5G and 6G network configurations and services. The demonstration is the latest milestone in a collaboration anchored at Northeastern University, where INSI has served as a nexus for industry-ac academic partnerships at the frontier of intelligent wireless networks. Over the past few years, Northeastern has built deep research ties with SoftBank on AI-native architectures, leading to multiple publications, AI-RAN Alliance demonstrations, and innovation in next-generation wireless. INSI collaborate with Keysight on AI-RAN testing and spun off zTouch Networks to commercialize technology on open, GPU-accelerated RAN infrastructure. This demo brings those threads together for the first time around a shared vision: truly autonomous, AI-driven radio access networks. At the heart of the demo is AgentRAN, developed at Northeastern's Open6G AI-RAN Alliance Lab. Operators express goals in natural language - such as "maximize throughput while prioritizing emergency traffic" - and the AgentRAN Manager decomposes these intents into actions for specialized agents operating across the RAN stack. The agents are powered by the SoftBank's Large Telecom Model (LTM), a purpose-built foundation model trained on telecom-specific KPIs, configurations, and domain data--f further optimized with high-fidelity training data generated using Keysight's RF digital twin channel emulation solutions with real world accurate 3D ray tracing capabilities (RaySIM, PROPSIM) for more accurate decisions than generic cloud models can deliver. The Northeastern Open6G AI-Ran lab, using Keysight's PROPSIM, demonstrates multiple agentic scenarios, deployed on an end-to-end programmable private 5G AI-RAN infrastructure, managed and orchestrated by zTouch Networks' zTouch.OS. Using Keysight's AI-RAN performance scoring solution, the system shows clear performance gains linked to the use of SoftBank's LTM, high fidelity training datasets generated by Keysight RF digital twin solutions, and zTouch Networks' zTouch".OS. Ankündigung • Jan 21
Softbank Corp. Announces "Infrinia AI Cloud OS," a Software Stack for AI Data Centers SoftBank Corp. announced that its Infrinia Team, which works on the development of next-generation AI infrastructure architecture and systems, has developed "Infrinia AI Cloud OS," a software stack designed for AI data centers. In addition, the software stack is expected to reduce total cost of ownership (TCO) as well as operational burden compared with bespoke solutions or in-house development. This will enable the rapid delivery of GPU cloud services that efficiently and flexibly support the full AI lifecycle--from AI model training to inference. SoftBank plans to deploy 'Infrinia AI Cloud OS' initially within its own GPU cloud services. Furthermore, the Infrinia Team aims to expand deployment to overseas data centers and cloud environments with a view to global adoption. As a result, user needs and usage patterns for AI computing are becoming increasingly diverse and sophisticated, and requirements including the following have emerging: Access to infrastructure that is fully managed by GPU cloud service providers, abstracted GPU bare-metal servers; Cost-optimized, highly abstracted inference services without concerning with GPU management; Advanced operations in which AI models are trained and optimized on centralized servers and deployed for inference at the edge. Building and operating GPU cloud services that meet these requirements requires highly specialized expertise and involves complex operational tasks, placing a significant burden on GPU cloud service providers. To address these challenges, the Infrinia Team developed "Infrinia AI cloud OS," a software stack that maximizes GPU performance while enabling the easy and rapid deployment and operation of advanced GPU cloud services. Key Features of "Infrinia AI CloudOS" Kubernetes as a Service: Reduces the operational burden of managing the physical infrastructure and the Kubernetes software layer by automating the entire stack (from BIOS and RAID settings to the OS, GPU Drivers, networking, Kubernetes Controllers and Storage) on GPU Platforms such as NVIDIA GB200 NVL72; Software-defined dynamic, on-the-fly physical connectivity (NVIDIA NVLink) and memory (Inter-Node Memory Exchange) reconfiguration, as the customers create, update and delete their clusters to suit their AI workload needs; Automatic node allocation based on GPU proximity and NVIDIA NVLink domain to reduce latency and maximize GPU-to-GPU bandwidth for highly distributed jobs; Enables users to deploy inference services simply by selecting Large Language Models, without working with Kubernetes or the underlying infrastructure; OpenAI-compatible APIs, enabling drop-in integration with existing AI applications; Seamless scaling across multiple nodes in core and edge platforms such as NVIDIA GB200 NV L72 and other platforms; Secure Multi-tenancy and High Operability; Tenant isolation through encrypted cluster communications and separation; Automation of operational maintenance, including system monitoring and failover; API environment for connecting to the AI data center's portal, customer management systems, and billing systems. These key features allow AI data center operators with customer management systems, as well as enterprises offering GPU cloud services, to add advanced capabilities that enable efficient AI model training and inference while flexibly utilizing GPU resources, to their own GPU service offerings. 
