AI is on the Edge and Network Jitter is Pushing It Over

Introduction

AI inference and training are rapidly becoming more distributed to enable low-latency, high-efficiency applications by processing data close to the source. As this trend accelerates, Gartner predicts that by 2027, 90% of organizations will adopt a hybrid or multi-cloud strategy, largely to meet the demands of distributed AI.¹ Many will turn to Multi-Cloud Networking (MCN) solutions, which promise seamless connectivity across cloud, edge, and on-premises environments.

Telcos see this shift as an opportunity to move beyond “dumb pipe” connectivity and become key AI service enablers. To capitalize on this, they are investing heavily in advanced edge computing and network infrastructure while aligning with MCN vendors.

Yet, distributed AI workloads—and applications in general—are constrained by a major bottleneck: packet delay variation (PDV), or jitter. Network jitter doesn’t just create a bottleneck—it inflates AI costs by wasting GPU/TPU cycles and slowing AI decision-making.

Beyond adding latency, jitter can cause network throughput to collapse, stalling AI workloads. This occurs because distributed AI relies heavily on TCP for guaranteed in-order packet delivery across multi-cloud and edge environments. However, TCP consistently interprets jitter as a sign of network congestion, and responds by throttling traffic and retransmitting packets to avoid data loss–even when bandwidth is available.

AI workloads confined to data centers avoid these issues by using RDMA to bypass the OS network stack. However, RDMA is impractical for edge AI due to its specialized infrastructure requirements and reliance on lossless networking, which wireless and cross-region cloud environments cannot guarantee.

Today’s networking solutions—including MCN and AI networking—fail to solve this problem. Some even make it worse.

This blog explores the sources of jitter that cripple distributed AI, why today’s network solutions fail to mitigate its impact, and how organizations can solve the problem—without expensive and disruptive infrastructure overhauls.

Distributed AI Workloads Are Both a Source of Jitter and Highly Sensitive to It

Several key factors amplify jitter’s impact on AI inference and training:

1. Unpredictable Data Flows

AI workloads generate random bursts of traffic, large payloads (e.g., embeddings and model outputs), and highly variable transmission patterns. This unpredictability creates jitter.

2. Virtualization Overhead

Virtualized cloud and edge environments introduce additional jitter as AI workloads compete for shared CPU, memory, storage, and network resources. Overlays like VXLAN and GRE further amplify delays with encapsulation/decapsulation overhead.

3. Cross-Region Communication and Synchronization Challenges

AI workloads that span multiple cloud and edge environments, require continuous data exchange between distributed nodes. Each network hop introduces queuing delays and variable processing times, increasing jitter. Additionally, large models are often sharded and require frequent synchronization across compute nodes. Jitter disrupts this process, stalling workloads and increasing GPU/TPU idle time.

4. Last-Mile Wireless Networks

Edge AI depends on 5G for high-speed, low-latency data transmission, but mmWave 5G suffers greater signal attenuation than LTE and most Wi-Fi bands, limiting its range and reliability. Unlike LTE, 5G requires a clear line of sight from sender to receiver. Obstacles can cause reflection, refraction, and diffraction, leading to multipath interference and variations in packet delivery times.

The Need for a New Solution

Distributed AI workloads need a solution that prevents TCP from unnecessarily reducing throughput in response to jitter—without requiring lossless networks or costly infrastructure overhauls. The root cause lies in TCP’s congestion control algorithms (CCAs), which operate at the transport layer (Layer 4 of the OSI stack). Most network performance solutions either don’t function at this layer or have limited effectiveness when they do. Some even worsen the issue, amplifying jitter and further degrading AI performance:

• Jitter Buffers – Work at the application layer (L7) by reordering packets to realign timing, but this creates random delays that degrade real-time application performance and introduce more jitter.
• Bandwidth Upgrades – A physical layer (L1) fix that provides only temporary relief because the root cause isn’t addressed. Traffic quickly scales to the new level of bandwidth and the incidence of jitter-induced throughput collapse goes up in tandem, leading to another round of expensive and disruptive upgrades.
• SD-WAN – Optimizes routing based on measurements at the edge, but has no control beyond that. What if all paths are bad, or network conditions suddenly change?
• QoS Techniques –Packet prioritization, traffic shaping, and bandwidth reservation don’t fix TCP’s flawed response to jitter. Some QoS methods add jitter by creating variable delays for lower priority traffic.
• 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 adjusts network parameters and reroutes traffic in response to changing network conditions, but often adds jitter by continuously modifying paths and resource allocations.

Since these solutions fail to fix TCP’s flawed jitter response—and many MCN and AI networking solutions rely on them—AI at the edge needs a fundamentally different approach. Apparently, it’s not easy to deliver one. Even MIT Research recently cited TCP’s CCAs as having a significant and growing negative impact on network performance because of their response to jitter, but couldn’t offer a practical solution.²  

What Edge Networking Really Needs for Distributed AI to Succeed

TCP’s CCAs would have to be modified or replaced to remove the bottleneck they create by their inability to differentiate between jitter caused by actual congestion versus other factors. However, for a solution to be viable in a production environment, it can’t require any changes to the TCP stack itself, or any client or server applications that rely on it.  It must also seamlessly integrate with ADCs, SD-WANs, VPNs, and other existing network infrastructure.  

 A Proven, Cost-Effective Solution

Only Badu Networks’ patented WarpEngine^TM carrier-grade optimization meets the key requirements outlined above for eliminating unnecessary jitter-induced throughput collapse. WarpEngine’s transparent single-ended proxy architecture means no modifications to client or server applications or network stacks are required. It works with existing network infrastructure, so there’s no rip-and-replace. WarpEngine determines in real-time whether jitter is due to congestion, and prevents throughput from collapsing and applications from stalling when it’s not. As a result, bandwidth that would otherwise be wasted is recaptured. WarpEngine builds on this with other performance and security enhancing features that benefit not only TCP, but also UDP, GTP (used by 5G and LTE) and other traffic. 

These capabilities enable WarpEngine to deliver network throughput improvements ranging from 2-10x or more for some of the world’s largest mobile network operators, cloud service providers, government agencies and businesses of all sizes.³ These dramatic results not only reflect jitter’s enormous and growing impact on network performance, but also the lack of alternatives to effectively deal with it. 

WarpEngine Delivers Unmatched Deployment Flexibility

WarpEngine is designed for maximum deployment flexibility in multiple form factors, making it adaptable to the diverse networking conditions AI workloads encounter in multi-cloud and edge environments:

• Core networks – Optimizes large-scale traffic in carrier networks, corporate data centers, and cloud environments.
• Edge locations – Deployable at LTE or 5G base stations, Wi-Fi access points, and enterprise network edges to enhance WAN, broadband, and FWA performance.
• Virtualized environments – WarpVM™, the VM form factor of WarpEngine, is built for AI workloads in cloud and virtualized edge environments. It installs in minutes on AWS, Azure, VMware, and KVM
• WarpVM is also certified by Nutanix™ for their Nutanix Cloud Platform (NCP)™ for hybrid multi-cloud deployments.4 With NCP, WarpVM has shown performance results consistent with the 2-10x throughput improvements demonstrated in other implementations.
  
  Nutanix Cloud Platform (NCP) also powers GPT-in-a-Box™, an AI infrastructure stack that integrates NVIDIA NIM, Hugging Face, and other leading AI frameworks. Deploying WarpVM alongside NCP and GPT-in-a-Box instantly eliminates jitter-induced throughput collapse across multi-cloud and edge networks, unlocking immediate benefits:
  • Minimized GPU/TPU idle time, maximizing compute efficiency and cutting costs.
  • Improved scalability, enabling AI workloads to scale seamlessly across distributed environments.
  • Lower latency, ensuring faster inference and real-time AI performance.

Conclusion: Distributed AI Can’t Afford Jitter—And Edge Networking Must Catch Up

MIT research has identified TCP’s response to jitter as a persistent bottleneck. It isn’t just a bottleneck—it’s a major cost driver. Every millisecond of jitter increases GPU/TPU idle time, slows AI decision-making, and inflates AI costs. Without a solution, AI workloads distributed across multiple cloud and edge environments will continue to underperform. But no widely-adopted solution has been able to overcome TCP’s flawed response to jitter—until now.

WarpEngine eliminates unnecessary jitter-induced throughput collapse, and adds other features that unlock 2-10x performance gains with existing infrastructure at a fraction of the cost of network and server upgrades.

Don’t let jitter stall your distributed AI workloads and inflate costs. See the difference firsthand—request a free trial of WarpEngine today.

Notes

1. Gartner: https://www.crnasia.com/news/2024/hybrid-cloud/gartner-90-of-organizations-will-adopt-hybrid-cloud-through
2. Starvation in End-to-End Congestion Control, August 2022:    https://people.csail.mit.edu/venkatar/cc-starvation.pdf
3. Badu Networks Performance Case Studies: https://www.badunetworks.com/wp-content/uploads/2022/11/Performance-Case-Studies.pdf
4. Nutanix Technology Partners: https://www.nutanix.com/partners/technology-alliances/badu-networks