Cell Towers are Poised to Become Edge AI Engines – But There’s a Catch

Introduction

Edge AI is no longer just a vision. As demand grows for real-time inference, adaptive services, and AI-driven automation, compute is moving closer to where data is generated and used — and cell towers are uniquely positioned to enable it. They already provide:

• Unmatched coverage, less than a millisecond from most users and devices
• Integrated backhaul connectivity to the core and cloud
• A 5G foundation for wide-area, low-latency edge AI.

Today, private 5G networks equipped with multi-access edge computing (MEC) capabilities lead edge AI deployments.¹ These networks can support Small Language Models (SLMs) for real-time local inference, using GPUs, SmartNICs, and onboard storage to power applications like IoT analytics and factory automation—unlocking new revenue and cost savings. 

Complementary networks, including fiber-based metro Ethernet, Wi-Fi 6/6E for dense indoor spaces, and private LTE/CBRS (shared spectrum networks) enhance 5G by extending coverage and supporting localized AI processing.

Public 5G supporting tower-based edge AI is just beginning to emerge. With traditional subscriber growth stagnating, telcos see edge AI’s potential as a major new revenue source. McKinsey estimates the global GPU-as-a-Service (GPUaaS) market for telcos at $35–70 billion annually by 2030, primarily driven by North America and Asia. ²  

That’s why telcos are racing to make their towers AI-ready. Many operators are already upgrading their infrastructure with advanced radio, spectrum, backhaul, and MEC infrastructure:

• T-Mobile is deploying Ultra Capacity 5G with mid-band and mmWave spectrum, Massive MIMO, and uplink carrier aggregation — achieving uplink speeds over 500 Mbps in tests.
• AT&T is enhancing private 5G and urban networks with Massive MIMO arrays (high capacity antennas) to support mission-critical workloads.
• T-Mobile’s “5G On Demand” uses portable small cells and dense urban colocation for better localized performance.
• Verizon Business and T-Mobile offer private 5G/LTE services integrated with MEC to deliver ultra-low latency analytics, AR/VR, and AI inference at the edge.
• U.S. operators are also adopting high-capacity E-Band and mmWave backhaul to deliver 10–20 Gbps between towers and the core to minimize latency under heavy loads.
• The AI-RAN Alliance, a consortium of telco and AI industry leaders such as Nvidia, is advancing AI integration into Radio Access Networks (RAN) to optimize 5G network performance as well as enable edge AI workloads.3

The Hidden Bottleneck

Enabling cell towers to support AI isn’t just a capacity and MEC infrastructure issue.

Distributed AI workloads rely heavily on TCP for guaranteed, in‑order packet delivery – critical for AI workloads that depend on deterministic outcomes. In telco networks, TCP packets carrying AI data are encapsulated in GPRS Tunneling Protocol (GTP) tunnels to traverse the 5G core, and are then decapsulated at the base station.

But TCP wasn’t designed for distributed AI’s bursty, highly parallel traffic patterns which create packet delay variation — or jitter. This jitter is compounded even further as devices move around, and more devices are added to an edge network. TCP consistently interprets jitter as a sign of network congestion, even when bandwidth is available. To avoid data loss, TCP retransmits unacknowledged packets and reduces throughput — leaving GPUs idle, network capacity underutilized, performance SLAs unmet, and AI costs climbing.

5G is heavily relied on by edge AI for its high data speeds.  However, its high frequencies, dense small cells, frequent handoffs, line-of-sight requirements and GTP encapsulation/decapsulation create unpredictable delays in packet transmission, amplifying jitter caused by distributed AI’s traffic patterns.

In controlled data center settings over a LAN, AI workloads avoid TCP’s jitter response by using RDMA to copy data directly from one server’s memory to another’s, bypassing the OS network stack entirely. However, RDMA is impractical for the edge, due to its specialized infrastructure requirements and reliance on lossless networking, which wireless edge and cross-region cloud environments cannot guarantee.

Why Not Just Replace TCP? 

Despite its flaws, TCP remains the backbone of distributed AI for good reason. TCP’s ordered, reliable delivery semantics make it ideal for AI workloads that depend on consistent results. Frameworks like PyTorch Distributed and Horovod are deeply tied to TCP, as are orchestration tools, monitoring platforms, and SLAs.

In other words, TCP is deeply embedded in the AI ecosystem—and ripping it out would come at prohibitive cost. While protocols like QUIC offer potential, they lack TCP’s widespread adoption, particularly in AI frameworks.

Replacing TCP would require rewriting distributed frameworks and re‑architecting cloud and edge stacks. It would also mean retraining operations teams and risking compatibility issues throughout the entire AI pipeline. The only practical solution is to fix TCP’s flawed jitter response.

Existing Solutions Fall Short

TCP’s congestion control algorithms (CCAs), operating at Layer 4 of the OSI stack, drive its flawed response to jitter. Most network performance solutions, however, either don’t work at this layer or are only marginally effective if they do. Some even worsen the issue, amplifying jitter and further degrading performance:

• Jitter Buffers: Operate at the application layer to reorder packets and realign packet timing, but these processes add random delays that ruin performance for real-time applications and become yet another source of jitter.
• Bandwidth Upgrades: Traffic quickly saturates new capacity, and the incidence of jitter induced throughput collapse goes up in tandem.
• SD‑WAN: Optimizes edge routing but can’t fix bad paths or rapid changes in network conditions.
• QoS Techniques: Prioritize traffic, but add variability for lower‑priority applications, amplifying jitter.
• TCP Optimization:  Adjusts congestion windows, uses selective ACKs, and modifies timeouts, but only improves performance by 10-15% since it doesn’t stop TCP from misinterpreting jitter as congestion.
• AI Networking: Dynamically reconfigures network parameters and reroutes traffic, but often amplifies jitter with its real-time adjustments.

Even MIT Research has flagged TCP’s CCAs as a growing challenge in today’s jitter-prone networks—yet offered no practical solution.⁴

Edge AI needs a fundamentally different approach to TCP’s jitter response, one that’s single-ended, works within existing network and client/server application stacks, and integrates with MEC and other infrastructure at the cell tower base station.

 A Proven and Cost-Effective Solution

Badu Networks’ patented WarpEngine™ delivers carrier-grade optimization designed to eliminate jitter-induced throughput collapse—making cell towers truly AI-ready. Its transparent, single-ended proxy architecture requires no changes to client or server applications, TCP stacks, or AI frameworks, and integrates seamlessly with existing tower infrastructure—no rip-and-replace required.

WarpEngine intelligently detects whether jitter is caused by congestion and prevents throughput collapse when it’s not—reclaiming bandwidth and compute capacity that would otherwise be wasted. Combined with other latency-reducing features, this enables WarpEngine to deliver 2–10× throughput improvements on existing infrastructure for leading mobile operators, cloud providers, enterprises, and government agencies—all at a fraction of the cost of server or network upgrades. It supports TCP, UDP, and GTP (used by 5G and LTE), ensuring consistent performance across the protocol mix that powers edge AI, where jitter and latency often lead to GPU and network underutilization.

 Juniper Networks’ research shows that just 20–50 microseconds of jitter can increase AI workload duration by 30% to 60% using NVIDIA A100 GPUs—even in highly controlled data center environments.⁶ In well-tuned private 5G MEC networks, average jitter has been measured at 270 microseconds, with some tests as low as 30 microseconds.⁷ Jitter is likely even greater in public telco 5G edge AI networks, due to factors like mmWave spectrum, frequent small cell handoffs, line-of-sight interference, and GTP encapsulation delays—compounded by dynamic RAN scheduling, multi-tenant cores, and variable backhaul. While no published field studies confirm this yet, the relationship between jitter and AI workload performance is non-linear in most networks, especially those relying on TCP. Even small increases in jitter can lead to disproportionate slowdowns due to GPU idle time, retransmissions, and synchronization delays. 

Still, we can conservatively estimate the impact using the high end of Juniper’s results range. For a telco with 1,000 MEC-enabled towers, each running 10 NVIDIA A100 GPUs at $3.50/hour ($306 million/year at optimal efficiency), 50 microseconds of jitter resulting in a 60% increase in workload duration would raise costs to $489.6 million—wasting $183.6 million annually on GPU runtime alone. WarpEngine’s proven 3× average throughput improvement in 5G deployments reduces this increase to just 20%, lowering GPU costs to $367.2 million and saving $122.4 million per year.  Actual savings are even greater, as improved tower throughput also boosts backhaul efficiency—maximizing ROI across the full network path from cell tower to core and cloud.

Deployment Flexibility 

WarpEngine is designed for maximum deployment flexibility to adapt to diverse edge, core and multi‑cloud AI environments, including:

• Cell Towers and Edge Sites: Deployed at 5G and LTE base stations as a hardware appliance, or as software on customer- or partner-supplied hardware, including MEC platforms and acceleration components like NVIDIA BlueField DPUs.
• Core Networks: Enhances throughput in carrier backbones, corporate data centers, and cloud cores.
• Virtualized & Cloud Environments: WarpVM™, WarpEngine’s VM form factor, installs in minutes on AWS, Azure, VMware, and KVM. WarpVM is also certified by Nutanix for their multi-cloud platform8 ,demonstrating similar performance results to those cited above.
• WarpVM mitigates the impact of random delays in packet transmission caused by competition for cloud resources between AI and other hosted applications–a significant source of poor cloud network performance that can ultimately impact edge AI.
• Multi-Cloud Networking (MCN) solutions:  WarpVM easily integrates with MCN vendor solutions that are becoming the backbone of many distributed AI deployments.

Conclusion

The real barrier to edge AI isn’t network capacity or lack of MEC infrastructure—it’s TCP’s reaction to jitter. Every millisecond of jitter increases GPU/TPU idle time, wastes backhaul bandwidth, slows AI decision-making, and inflates AI costs.  

By combining WarpEngine with MEC hardware and other infrastructure, telcos can transform their cell towers into high‑performance edge AI engines — delivering the low‑latency and scalability edge AI workloads require.

The business case is equally compelling: by reducing jitter-induced inefficiencies, telcos can support more AI workloads on existing infrastructure, eliminating the need for costly upgrades. Getting past this bottleneck could unlock billions in new revenue—turning today’s cell towers into tomorrow’s profit centers.

See the difference firsthand—request a free trial of WarpEngine to test at one of your cell tower base stations today.

Notes:

1. https://www.fierce-network.com/wireless/ericssons-latest-report-says-private-5g-will-help-unlock-ai-and-iot
2. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ai-infrastructure-a-new-growth-avenue-for-telco-operators
3. AI-RAN Alliance, “Industry Leaders in AI and Wireless Form AI-RAN Alliance,” 2024, https://ai-ran.org/news/industry-leaders-in-ai-and-wireless-form-ai-ran-alliance/
4. Starvation in End-to-End Congestion Control, August 2022:    https://people.csail.mit.edu/venkatar/cc-starvation.pdf
5. Badu Networks Performance Case Studies: https://www.badunetworks.com/wp-content/uploads/2022/11/Performance-Case-Studies.pdf
6. https://community.juniper.net/blogs/mohan-kumar-m-v/2025/06/05/the-hidden-cost-of-jitter-in-aiml-training-fabrics
7. https://www.mdpi.com/2079-9292/12/6/1310
8. Nutanix Technology Partners: https://www.nutanix.com/partners/technology-alliances/badu-networks