??DeepSeek & Key Challenges in AI Implementation for Telecom
Telecom companies expect more affordable AI models and new opportunities in edge computing, while equipment suppliers have struggled following the DeepSeek announcement.
The uncertainty is evident in Nvidia’s stock performance. On January 27, its share price plummeted 17%, erasing hundreds of billions in market value, as investors feared a slowdown in GPU production. However, a day later, the stock rebounded by 9% after what many saw as an overblown reaction.
While this isn’t directly related to telecom, the ??ripple effects have clear implications for AI users, including telcos.
In this short article let's talk about possible challenges related to implementation of Large Language Models (LLMs) in 5G infrastructure and how open-source models like DeepSeek can help telco operators to overcome them.
The emergence of DeepSeek’s latest AI model, DeepSeek-R1, has the potential to finally overcome main challenges of AI implementation in 5G networks. Telecom operators, infrastructure vendors, and other industry players have long faced challenges in deploying AI due to infrastructure limitations, security concerns, and massive cost constraints.
However, DeepSeek is a result of an innovative approach to AI development.
Key Challenges in AI/LLM Implementation for Telecom??
Before the introduction of DeepSeek-R1, telecom networks struggled with several major challenges related to LLM deployment, including:
???First of all this is security and compliance risks: many AI workloads require sensitive data processing. Running AI applications across different mobile network nodes or any unknown locations (mid routers and other 3rd parties network devices) can creates security and compliance concerns.
??Second issue related to fragmentation in AI inferencing: if we supposed to have AI models in mobile networks it may require inferencing across nodes owned by multiple operators. And usually developers (especially in case of proprietary models) do not want to negotiate separate AI inferencing-as-a-service (AIaaS) deals with different operators in the same market, let alone on a global scale.
??Next is the fact that there are interconnect and network limitations: usually AI applications need fast and secure pathways to interact with multiple cloud services, hyperscalers, and security platforms. However, telecom networks often lack dense interconnect points, leading to bottlenecks and high costs. Moreover, in most of the cases there is an opposite picture: security GWs, firewalls and so on between Core network and public/Internet. Not taken into account the fact that 5G by design have only UPF acting in a role of single connection point with external networks.
??More obvious fact is infrastructure constraints: in most of the cases AI workloads require dedicated hardware, which differs from the general-purpose platforms traditionally used in telecom infrastructure. Current mobile networks and radio access networks (RANs) are not optimized for AI applications. Probably Open RAN approach may help in that way by introducing the ability to build 5G RAN on suitable AI-supported HW.
??And finally - energy and sustainability concerns: Telecom companies are constantly under pressure to reduce their carbon footprint. AI workloads would definitely consume substantial energy, creating conflicts with sustainability initiatives, especially in regions with strict energy regulations.
How DeepSeek-R1 can help to overcome these challenges
The thing is that DeepSeek-R1 introduces several innovations that can help telecom operators navigate abovementioned AI deployment challenges:
??More Efficient AI Models
Unlike traditional AI models, which require INSANELY huge computational resources, DeepSeek-R1 leverages a "mixture of experts" system. This approach activates only relevant parts of the network instead of processing data through an entire large-scale model.
What does it mean for telecom networks?
??Enhanced Security and Privacy
DeepSeek’s reinforcement learning approach would definitely help the model perform reasoning without requiring step-by-step solution datasets. This means telecom companies can train AI models without exposing sensitive data to external nodes, cloud providers or any 3rd companies. By keeping data processing within telecom networks, operators can:
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??Solving Fragmentation with Open-Source AI
DeepSeek’s open-source nature makes it easier for telecom vendors to integrate AI solutions across different mobile networks. Unlike proprietary AI models that require expensive licensing and probably any sorts of cloud-based access, DeepSeek enables:
??AI Optimization for RAN and Edge Computing
One of the biggest obstacle in AI deployment for telecom networks is the lack of dedicated AI hardware within radio access networks (RAN). DeepSeek’s model distillation techniques allow AI models to be compressed and optimized for smaller-scale hardware, making AI integration into RAN more feasible with about any large "data centers" with insane amount of GPU/TPUs and appropriate cooling and power systems. As a result:
?Addressing Energy and Sustainability Concerns
DeepSeek’s cost-efficient training methods significantly significantly reduce the need for massive GPU clusters, which traditionally consume vast amounts of power. This would definitely aligns well with telecom operators’ sustainability goals:
Conclusion
I suppose that DeepSeek's breakthrough in efficient AI model training and deployment shows a significant opportunity for the telecom industry. While some challenges remain, particularly in terms of infrastructure adaptation and standardization issues, the reduced resource requirements and improved efficiency make AI implementation more feasible than ever before. That's the main point!
I believe that for telecom operators and any sorts of vendors, the path forward likely involves a hybrid approach: for example, utilizing DeepSeek's innovations for MEC computing and specialized network functions and at the same time maintaining traditional infrastructure for core operations. The key to success will be carefully balancing the benefits of advanced AI capabilities with the practical constraints of telecom network operations.
What's more is the fact of democratization of AI technology (through innovations like DeepSeek and basically open-source approach per se) could accelerate the transformation of telecom networks into truly intelligent systems with specific LLM capabilities tailored to specific 5G needs.
However, this transformation will require careful planning, securing ROI by real use cases and anyway substantial investment in infrastructure updates.
If you're a 5G engineer, AI researcher, or decision-maker, you NEED to understand how LLMs like DeepSeek can fit into the future of Telecom.
That’s why I’ve put together a 2.5-hour Udemy course where I break it all down for you:
?? AI/ML basics for telecom
?? Generative AI adoption & risks
?? LLMs for 5G Networks, MEC, & on-device AI
?? AI infrastructure challenges
??Join. Learn. Change.??
Supply Chain Executive at Retired Life
2 个月DeepSeek AI Breakthrough – Cheat Sheet. “The DeepSeek breakthrough is due to cutting-edge AI performance, open source code, and greatly reduced computational power.” ~Dave Waters https://www.supplychaintoday.com/deepseek-ai-breakthrough/