Traditional telecommunications infrastructure has long relied on manual configuration and semi-automated processes, leading to operational inefficiencies and lengthy deployment cycles. While AI has transformed many industries, its application in telecom network operations has been limited by the complexity of network systems and the need for highly specialized domain knowledge. SoftBank’s development of the Large Telecom Model (LTM) represents a significant breakthrough in addressing these challenges.
SoftBank, a multinational conglomerate holding company, has developed a large language model (highlighted at https://www.softbank.jp/en/corp/news/press/sbkk/2025/20250319_03/) specifically designed for the telco industry. This model, referred to as the Large Telecom Model (LTM), has been trained on a wide array of datasets that encompass both network data and operational knowledge. The LTM’s purpose lies in its ability to facilitate advanced inference and problem-solving capabilities in the design, management, and operation of cellular networks.
SoftBank’s LTM Aims to Bridge AI and Network Engineering
SoftBank’s Large Telecom Model (LTM), trained on extensive datasets, can potentially enhance the telecommunications industry. For instance, the LTM could optimize network architecture during the design phase, ensuring efficiency and coverage. Additionally, the LTM could provide insights into network performance, enabling proactive identification and resolution of potential issues. The LTM could also automate routine tasks during network operation, allowing human operators to focus on more complex issues. Overall, SoftBank’s development of the LTM represents a significant advancement in the application of AI within the telecommunications sector, with the potential to drive innovation and efficiency.
Fine-tuning Capabilities:
- The Large Language Model (LLM) demonstrates adaptability through fine-tuning, allowing for the creation of specialized AI models tailored to specific use cases within the telecommunications industry.
- By fine-tuning the LLM on relevant datasets, engineers and researchers can develop AI models that excel in highly specialized tasks. A prime example of this is the development of models that predict optimal base station configurations with an accuracy exceeding 90%.
- This fine-tuning capability not only enhances accuracy but also drastically improves efficiency. Tasks that once took days, such as configuring base stations, can now be completed in mere minutes, thanks to the power of fine-tuned AI models.
Technical Performance:
- The LLM’s technical performance has been significantly enhanced through optimization using NVIDIA’s Network Infrastructure Management (NIM) platform.
- This optimization has resulted in a fivefold improvement in Time to First Token (TTFT), a crucial metric indicating the responsiveness of the model. Additionally, the Tokens Per Second (TPS) metric, which measures the model’s throughput, has also been improved fivefold.
- NVIDIA NIM further offers deployment flexibility, enabling the LLM to be deployed both on-premises and in the cloud, catering to diverse operational requirements.
AI Integration in Network Operations:
- The LLM serves as a cornerstone for the “AI for RAN” (Radio Access Network) initiative, paving the way for intelligent automation in network management.
- Its capabilities are expected to underpin the development of sophisticated AI agents that can autonomously design and optimize networks.
- SoftBank, a key player in the telecommunications industry, has ambitious plans to integrate LLM-based AI models with its “AITRAS” orchestrator, a move that promises to streamline network operations and enhance overall network performance.
Use Case Examples:
- New base station deployment: The LLM can play a pivotal role in the deployment of new base stations, especially in challenging environments such as high-density urban areas. By analyzing vast amounts of data, it can generate optimal configurations that ensure seamless connectivity and maximize network efficiency.
- Existing base station reconfiguration: The LLM’s ability to adapt to changing conditions makes it invaluable for reconfiguring existing base stations. Whether it’s to accommodate a special event or to respond to shifts in traffic patterns, the LLM can swiftly generate optimized configurations that meet the dynamic demands of the network.
- Network Anomaly Detection: By continuously analyzing network traffic and performance data, the LLM can be trained to detect anomalies that may indicate potential issues or security threats. This proactive approach allows for rapid response and minimizes network downtime.
- Predictive Maintenance: The LLM’s predictive capabilities can be leveraged to forecast potential equipment failures or performance degradation. This enables network operators to schedule maintenance proactively, preventing costly outages and ensuring optimal network reliability.
- Customer Service Optimization: The LLM can be used to analyze customer service interactions, identify common issues, and provide personalized recommendations to improve customer satisfaction. This can lead to more efficient and effective customer support, ultimately enhancing the overall customer experience.
Development Infrastructure:
- Hardware: NVIDIA DGX SuperPOD was utilized for the distributed training of the Large Language Model (LLM), enabling efficient and scalable processing of vast datasets.
- Software: Future development plans include incorporating NVIDIA Aerial Omniverse Digital Twin (AODT) for simulating and validating configuration changes within the mobile network environment. This will allow for testing and optimization of network adjustments before implementation, reducing the risk of disruptions and improving overall network performance.
Technical Collaboration:
- Partnership: SoftBank and NVIDIA have partnered on NIM Microservices Optimization for Inferencing, aiming to enhance the efficiency and speed of AI inferencing within the mobile network. This collaboration leverages the expertise of both companies to optimize microservices architecture and improve resource utilization.
- Teamwork: The development process involved close collaboration between SoftBank’s RIAT Silicon Valley Office and the Japan team, fostering knowledge sharing and cross-cultural cooperation to achieve project goals.
Future Directions:
- Ongoing Research: Continued research and development efforts will focus on enhancing mobile network efficiency, exploring innovative techniques and algorithms to optimize resource allocation, reduce latency, and improve overall network performance.
- New Services and Experiences: The project aims to explore and develop new services and higher-quality network experiences for mobile users, leveraging AI and machine learning to personalize services, anticipate user needs, and deliver seamless connectivity.
- Network AI Agents: Future possibilities include the development of specialized network AI agents capable of performing specific tasks such as network planning, configuration, and optimization. These intelligent agents could automate complex processes, improve decision-making, and enable proactive network management.
Conclusion
The development of Large Language Models (LLM) like LTM presents developers with opportunities in AI model fine-tuning, distributed training, and integration of AI models with existing telecom infrastructure. Additionally, it emphasizes the importance of domain-specific knowledge in AI model development for specialized industries like telecommunications. The LTM, trained on SoftBank’s network data and operational expertise, exemplifies the potential of domain-specific AI models in telecom operations by achieving 90% accuracy in base station configuration predictions and reducing setup times from days to minutes. This proves AI’s capability to reliably manage complex network operations.