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SoftBank Streamlines Enterprise AI Workloads with Automated GPU Infrastructure
Published: 2026/01/21 13:26:44
Enterprises are increasingly adopting artificial intelligence (AI) and machine learning (ML) to drive innovation and efficiency. Though,deploying and managing the underlying infrastructure – notably GPU clusters – can be a notable hurdle. SoftBank has announced new software designed to address these challenges, offering automated Kubernetes and inference services to simplify AI infrastructure management.
Kubernetes-as-a-Service: Automating the infrastructure Stack
A core component of SoftBank’s offering is a Kubernetes-as-a-Service solution. This service automates the entire infrastructure stack, starting from low-level configurations like BIOS and RAID settings, extending through the operating system, GPU drivers, networking, Kubernetes controllers, and storage. This level of automation significantly reduces the complexity traditionally associated with setting up and maintaining GPU clusters.
The system dynamically reconfigures physical connectivity using Nvidia NVLink – a high-speed interconnect – and optimizes memory allocation as clusters are created, updated, or deleted. By strategically allocating nodes based on GPU proximity and NVLink domain configuration, the software minimizes latency and maximizes performance.This is crucial for demanding AI workloads that require rapid data transfer between GPUs.
Addressing Key Enterprise Pain Points
According to SoftBank, enterprises often struggle with several key challenges related to GPU infrastructure. These include complex GPU cluster provisioning,managing the lifecycle of Kubernetes deployments,scaling inference services,and fine-tuning infrastructure for optimal performance. These tasks typically require specialized expertise,which can be costly and challenging to acquire.
SoftBank’s automated approach aims to alleviate these pain points by handling the intricate details of BIOS-to-Kubernetes configuration,optimizing GPU interconnects,and abstracting inference into easy-to-use API-based services. this allows data science and machine learning teams to concentrate on model development and innovation, rather than being bogged down in infrastructure maintenance.
Inference-as-a-Service: Simplified Model Deployment
The second key service offered by SoftBank is Inference-as-a-service. This component enables users to deploy inference services – the process of using trained AI models to make predictions – by simply selecting the desired large language model (LLM). The service eliminates the need for manual Kubernetes configuration or underlying infrastructure management.
SoftBank’s Inference-as-a-Service provides APIs compatible with OpenAI, allowing for seamless integration with existing AI workflows. it is designed to scale across multiple nodes, leveraging platforms like the Nvidia GB200 NVL72 to handle demanding inference workloads efficiently. [[2]]
Enhanced Security and Management Features
The software also incorporates robust security and management features. These include tenant isolation through encrypted communications, automated system monitoring and failover capabilities, and APIs for integration with existing portal, customer management, and billing systems. These features are essential for ensuring the security, reliability, and scalability of enterprise AI deployments.
Key Takeaways
- SoftBank’s new software automates GPU infrastructure management, simplifying AI deployments for enterprises.
- kubernetes-as-a-Service streamlines cluster provisioning and configuration.
- Inference-as-a-Service enables easy deployment of large language models.
- The solution prioritizes security, scalability, and integration with existing
