Telecom-trained LLMs, or telecom-specific large language models, are advanced AI systems designed to process and understand natural language, tailored specifically for the telecommunications industry.
Unlike general-purpose LLMs (like those used in chatbots or content generation), these models are fine-tuned or trained on vast amounts of telecom-related data, such as technical documentation, network logs, customer service interactions, industry standards, and operational protocols.
This specialized training enables them to grasp the unique terminology, concepts, and challenges of the telecom domain—think terms like “baseband,” “latency,” “QoS” (Quality of Service), or “RAN” (Radio Access Network). In practice, telecom-trained LLMs can interpret complex network data, assist in troubleshooting, generate actionable insights, or even automate responses to customer queries with a deep understanding of the context.
For example, Nokia’s implementation, as mentioned in their recent announcement, uses these models for proactive threat detection—analyzing patterns in network traffic to spot security risks—or for streamlining service management by quickly parsing and acting on telecom-specific workflows.
Their strength lies in combining the natural language prowess of LLMs with domain expertise, making them highly effective tools for tasks like anomaly detection, fault prediction, or optimizing autonomous networks. Essentially, they’re like super-smart assistants who speak the language of telecom fluently.
Telecom-trained Large Language Models
Telecom-trained Large Language Models (LLMs) are specialized AI systems fine-tuned or built from scratch to address the unique needs of the telecommunications industry.
They leverage telecom-specific datasets—such as network logs, technical standards (e.g., 3GPP specifications), customer interactions, and operational data—to perform tasks like network management, troubleshooting, and customer support. Here are some examples of telecom LLMs based on recent developments and initiatives:
Nokia’s Telecom-Specific LLMs
Nokia has integrated telecom-trained LLMs into its autonomous networks portfolio, as announced around MWC25 (February 2025). These models, developed through Nokia Bell Labs, are trained on telecom data to enable proactive threat detection, anomaly identification, and network fault prediction. They’re designed to handle tasks like interpreting network telemetry and accelerating service creation for Communication Service Providers (CSPs). While not explicitly named, these LLMs are part of Nokia’s broader Agentic AI framework.
Tech Mahindra’s Multi-Modal Network Operations LLM
Launched in early March 2025, Tech Mahindra’s multimodal LLM is built in collaboration with NVIDIA, AWS, and Meta, using the Llama 3.1 8B Instruct model as a base. This LLM is tailored for telecom operators to automate network operations, enhance efficiency, and manage complex workflows. It processes diverse data types (text, telemetry, etc.) and is positioned as a transformative tool for network management.
TelecomGPT
Proposed in a 2024 research paper, TelecomGPT is a framework for adapting general-purpose LLMs into telecom-specific models. It involves pre-training on telecom datasets (e.g., standards, patents, and research papers) and fine-tuning with instruction and preference datasets for tasks like telecom math modeling, open-ended Q&A, and code generation in languages like Python or C++. While still experimental, it represents an academic push toward standardized telecom LLMs.
Ericsson’s TeleRoBERTa, TeleDistilRoBERTa, and TELECTRA
Ericsson has adapted general-purpose models like RoBERTa and ELECTRA for telecom use by pre-training them on 21GB of telecom-specific text data, including 3GPP specifications and internal Ericsson documentation. These models, developed around 2022, excel in tasks like trouble report classification, question-answering for support engineers, and test case generation, showcasing early adoption of telecom LLMs.
GSMA Foundry’s Open-Telco LLM Benchmarks Initiative
Launched in February 2025, this global community effort, supported by organizations like Hugging Face and Deutsche Telekom, doesn’t create a specific LLM but benchmarks existing models (e.g., GPT-4, Llama) for telecom tasks. It aims to refine LLMs for accuracy and security in areas like automation and energy efficiency, potentially leading to standardized telecom-optimized models.
These examples highlight a mix of proprietary, collaborative, and research-driven efforts to create LLMs that understand telecom jargon, standards, and operational needs.
Unlike general-purpose LLMs (e.g., ChatGPT), these models are fine-tuned or built to tackle domain-specific challenges, such as interpreting 5G protocols or optimizing network performance. The field is rapidly evolving, with more telecom LLMs likely to emerge as companies and researchers refine their approaches.
Expert Insights
In this TelecomTV webinar they ask how will the development of AI large language models (LLMs) impact telcos? Last year they heard strong arguments for and against telcos developing their own LLMs. And these LLMs are now starting to emerge.
Earlier this year, SKT, Deutsche Telekom, e&, Singtel and SoftBank Corp. announced the formation of a joint venture to develop specialised LLMs for telcos, operating across different languages and supporting a customer base of 1.3 billion people. So what is the LLM opportunity for telcos? And how will they be developed, implemented and monetised?
Panel
- Aaron Boasman-Patel, Vice President Innovation, TM Forum.
- Michael Clegg, Vice President and General Manager for 5G and Edge, Supermicro.
- Scott Cadzow, Chair of ETSI TC Securing Artificial Intelligence (SAI).
- Shujaur Mufti, Senior Manager, Global Partners Solution Architecture Telecom, Media, & Entertainment, Red Hat
