Telecommunications networks shuttle unimaginable amounts of data between devices, towers, and data centers; through wireless connections and fiber optic cables, every second of every day. AI’s ability to analyze massive data sets and derive insights that no individual or team could manage makes the telecom space a perfect place to deploy AI-based solutions. The cases are used there, and so is the value.
Most of the discussion you see today about AI focuses on new models and training. Inferencing is the other side of the AI equation. And as telecom companies optimize their networks and services with AI, they should understand the following points about AI inference, its demands, and challenges, and how to meet them.
Hold The Phone: What is AI Inference in Telecom?
Making today’s AI solutions work is a two-part process. The first part is training, wherein massive data sets are collected, and the AI’s algorithms are trained to look for patterns throughout the data. After it is trained comes AI inference, which is the process by which the solution puts what it knows into action to return useful answers to queries from an end-user.
AI inference has many potential applications for keeping telecom data flowing, preventing outages, and creating happier customers. These next use cases will demonstrate how.
Key Use Cases for AI Inference in Telecom
Network Optimization
Telecom AI solutions can watch network traffic moving through towers, determine which ones will get overwhelmed, and reroute traffic accordingly. AI-based automation and optimization keeps calls and texts moving more smoothly than manually reacting to data logs.
Predictive Maintenance
Early signs of wear and tear on telecom hardware that might not be obvious to an individual, but AI can detect them instantly. Using AI, a telecom company can address degenerating towers and cables before they create service disruptions.
Customer Experience
AI-based innovations like text- and voice-based virtual assistants stand to streamline the customer experience for telecom as they are in other verticals.
Security and Fraud Detection
AI monitoring solutions can identify anomalous traffic and other signs that indicate malicious behavior on a network, and either address it automatically or flag it for a cybersecurity professional, preventing data breaches.
Enhanced Customer Experiences through AI-Enabled Call Centers
A significant 84% of telecom professionals report AI boosting annual revenue, with 21% seeing an over 10% increase. Additionally, 77% note reduced operating costs, highlighting AI's strategic value in telecom customer service for streamlining operations, personalizing interactions, and empowering agents.
Based on recent reports, Vodafone has seen significant business ROI from its AI initiatives, according to NexGen Cloud in March 2025. Then after implementing its AI chatbot, Tobi, Vodafone experienced a 70% reduction in cost-per-chat/ The introduction of their next-generation AI chatbot, Super Tobi, in Portugal led to a substantial improvement in customer experience metrics, with the first-time resolution rate increasing from 15% to 60% and the online Net Promoter Score (NPS) jumping by 14 points to 64.
While these improvements in efficiency and customer satisfaction directly contribute to a positive business ROI and inspire innovation in AI hardware, the pace of CPUs has not kept pace with GPU innovation.
Take a Fresh Look at Your AI Hardware – Take the Next Leap
Massive growth defines AI in the global telecommunications market. According to recent 2025 date from Precedence Research, AI is set to explode from $3.34 billion in 2024 to $50.21 billion by 2034, a staggering 38.81% CAGR, highlighting rapid AI adoption across the telecom industry.
As AI models grow in volume and complexity, now is the time to take a hard look at your hardware efficiency. Start considering options by testing legacy servers and new AI accelerators with NeuReality’s new breed of AI-CPU versus traditional outdated x86 architecture upon which all GPUs are built today.
Real-Time Decision Making
Finding out about a service outage by a barrage of irate customer calls is a less-than-ideal way to do business. With AI monitoring networks, a telecom can be proactive in making decisions based on a full view of the current state of the network to maintain uptime and service quality, rather than having to react to problems and put out fires that have already started.
Cost Efficiency
AI's revolutionary potential in telecommunications is hampered by data center infrastructure limitations, leading to excessive costs, power consumption, and complexity with models like ChatGPT, Mistral, Llama, DeepSeek, Qwen, and Roberta running on outdated systems.
Traditional CPUs bottleneck AI inference in telecoms, hindering GPU efficiency despite their advancements. For cost-effective, scalable AI, NeuReality's NR1 Chip is a game-changer. This AI-CPU, purpose-built for inference orchestration, maximizes GPU utilization to near 100%—a significant leap from under 50% with x86 CPUs. This eliminates performance drops as AI workloads grow, delivering cost and energy-efficient AI at scale.
AI-CPU Replaces the Traditional CPU/NIC Architecture
The NR1 chip is the perfect complement to any GPU or AI accelerator and replaces outdated host CPU/NIC architecture (as seen below). Common contributors to the CPU performance bottleneck include pre- and post AI data processing, data loading, video and audio encoding and networking.
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NeuReality’s NR1 Chip eliminates the CPU bottleneck by subsuming it and the NIC into one but packing 6x the processing power to manage any AI workload. (See NR1 Chip below replacing both the NIC and general-purpose CPU.)
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NR1 features a low-power engine that combines CPU functionality with dedicated media and data processors, our patented AI-Hypervisor™ hardware IP, and AI-Over-Fabric™ networking engine.
The NR1 Chip’s primary function is to optimize and orchestrate the execution of already trained AI models on hardware accelerators like GPUs, FPGAs, and ASICs. It deals with receiving data, preprocessing, and the efficient flow of the AI data type (image, audio, video, text).
Challenges in AI Inference Deployments
While there is a world of value to be found for telecoms that deploy AI inference servers, they should also be aware of the following challenges:
Data Privacy and Compliance
Telecom is a heavily regulated area. Adhering to privacy best practices and maintaining compliance with data protection regulations cannot be overlooked when using AI solutions.
Infrastructure Needs
AI solutions are processor hungry. To run them without crashes, severe lag, or sky-high electric bills, AI data centers must deploy specialized AI-inferencing optimized hardware. The new release of the generative and agentic-AI ready Inference Appliance delivering 3x faster time-to-value and 6x lower total cost per million tokens. Comparing apples and apples, this proof-of-concept result ran Llama 3.3 70B on an AI-CPU powered Appliance versus x86 CPU for the same GPUs and AI Accelerators. That is AI economics redefined and enables more businesses to enter, experiment, innovate and fully deploy AI use case by use case.
Legacy System Integration
Telecoms vary greatly in how up to date they are technologically. Individual companies must determine if AI solutions will work with a given network, or if using them will require upgrades or even a rip-and-replace of existing infrastructure.
If you are expanding or building a new call center with on-premises inference servers or buying cloud compute, ask NeuReality, your solution provider or OEM to compare the price/performance differences between AI-CPU versus traditional x86 CPU on the same model, type, and numbers of GPUs. You will be impressed by the 50-90% price/performance gains with the AI-CPU.
Meeting New AI Challenges with NeuReality
In telecom, AI is readily solving tough, long-standing issues, but more can improve with the right hardware.
Our purpose-built NR1 Chip, the core of the NR1® Module, seamlessly integrates with any GPU. Bring your own AI accelerator for seamless integration inside our NR1® Appliance. Experience enhanced performance, efficiency, and per-token improvements comparing the same AI accelerator with NR1 versus traditional x86 architecture.
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