AI Training vs. Inference: Designing Networks for Real-World AI Workloads

The same AI model can require significant differences in infrastructure depending on whether you're training it or running it. Here's why your network architecture must account for both.

If you’ve spent any time in the trenches of AI infrastructure, you’ve probably seen it: the surprise when a meticulously engineered network, built with the latest hardware and best practices, suddenly falls short. The culprit? A mismatch between the network’s design assumptions and the actual demands of AI workloads - especially when it comes to the critical differences between training and inference.

This isn’t a theoretical problem. Across the artificial intelligence industry, organizations are investing millions in state-of-the-art GPU clusters and high-speed fabrics, only to discover that performance bottlenecks emerge not from lack of bandwidth, but from a fundamental misunderstanding of how AI workloads interact with the network. The result is often underutilized GPUs, frustrated data scientists, and a scramble to retrofit infrastructure that was “modern” just a year ago.

Understanding the Two Faces of AI Workloads

At first glance, the distinction between AI training and AI inference seems straightforward. Training is where the machine learning model learns from large data sets; inference is where the trained AI model is used to make predictions or decisions on new data. But as any practitioner who has deployed large AI systems in production will tell you, the infrastructure implications are anything but simple. In fact, the deeper you go, the more you realize that AI inference is not a monolith—it’s a spectrum, and the requirements can be as demanding and nuanced as training itself.

Training: The Synchronized Orchestra

Distributed AI training is the ultimate stress test for data center networks. Here, multiple GPUs (sometimes thousands) must work in lockstep, synchronizing gradients and parameters at every iteration. This all-to-all communication pattern is relentless—a firehose of data that can bring even the most advanced fabrics to their knees if not engineered correctly. The goal is to maximize throughput and minimize the job completion time, so every inefficiency in the network translates directly into wasted capital and slower innovation.

What does this look like in practice? Imagine a deep learning training job for a large language model (LLM), spread across 512 GPUs. Each GPU processes a chunk of input data, but after every batch, all GPUs must exchange their gradients—an operation called all-reduce. Every link in the network is stressed, and the slowest connection sets the pace for the entire job. Inadequate bandwidth, oversubscription, or a single misconfigured switch can cause the entire cluster’s utilization to plummet. This is why hyperscalers invest in high-performance, non-blocking, lossless fabrics and why even enterprises with “modern” networks often find themselves chasing bottlenecks as their AI ambitions scale.

Key technical takeaways:

• AI training jobs are bandwidth-hungry and latency-sensitive at synchronization points.

• The network must support high-throughput, all-to-all communication with minimal jitter.

• Even small inefficiencies can lead to millions in wasted GPU investment.

• The right infrastructure can optimize the use of computational resources and accelerate time-to-value for machine learning models.

Inference: From Stateless Simplicity to Agentic Complexity

AI inference, on the other hand, is often assumed to be simple. For small, quantized models, it can be stateless and horizontally scalable—think image classifiers, chatbots, or basic Q&A bots, easily load-balanced across commodity servers or even edge AI devices. But as AI models grow beyond 70B parameters and up, serving a single inference may require multiple GPUs working in concert, with high-speed backend links shuttling data between them. Unlike training’s all-to-all synchronization, large-model inference often involves pipeline-oriented communication: sequential, latency-sensitive, and highly variable depending on the model’s architecture.

The New Era: Agentic and Reasoning Models

The landscape is shifting rapidly. The rise of agentic and reasoning models—a big topic at GTC 2025—means AI inference is no longer just a single prompt and response. Now, a single user request can trigger cascades of internal model calls, external API queries, and multi-step reasoning chains. This creates stateful, session-based inference with complex orchestration and persistent context, demanding session affinity, caching, and robust internal networking. The infrastructure impact? Dramatically higher bandwidth, lower latency requirements, and a need for dynamic resource allocation.

Why does this matter? In stateless AI inferencing, you can throw more servers at the problem and let a load balancer do its thing. But in agentic inference, you need to keep context - sometimes across dozens of internal steps and multiple models. Session affinity becomes critical: if a user’s conversation or workflow hops between servers, you lose the benefit of cached context, and latency spikes. The network fabric must support not just north-south traffic from clients, but also east-west flows between inference nodes, orchestrators, and external APIs.

Example: Consider a customer support agent powered by a large language model. The user’s question might trigger a chain of sub-questions, database lookups, and calls to external tools. Each step may require coordination between multiple GPUs and services, all within tight latency budgets. The network is no longer just a conduit, it’s an active participant in the AI workflow. In real-time decision-making scenarios, such as autonomous vehicles or healthcare diagnostics, the need for low latency and reliable AI inference is even more critical.

Multi-GPU Inference: When Serving Looks Like Training

When serving massive models, AI inference can resemble distributed training in its need for backend GPU networking. Techniques like tensor parallelism and pipeline parallelism split the model across multiple GPUs and nodes, forcing inference to traverse the network in a pipeline or staged fashion. The first output token (in LLMs) often forms a critical path, requiring the user’s entire input data to be processed through all model layers—sometimes across multiple GPUs. Subsequent tokens may rely on cached intermediate results, shifting the bottleneck from compute to memory and bandwidth. Disaggregated serving architectures that dedicate some GPUs to the compute-heavy prefill phase and others to the memory-bound decode phase—can improve utilization, but at the cost of additional network traffic to shuttle data between stages.

In practice:

• The network must support both bulk data transfers (prefill) and low-latency, high-frequency exchanges (decode).
• Inefficient routing or congestion can cause “tail latency” spikes, degrading user experience in real-world AI applications.
• Specialized inference runtimes, like NVIDIA Triton or Dynamo, are emerging to orchestrate these complex workflows and optimize for high-performance, low-latency AI inferencing.

Stateless vs. Stateful Inference

Not all AI inference is created equal. Stateless inference is conceptually straightforward: a single request, a single response, no memory of prior interactions. This is how many REST APIs for AI solutions are structured, and it’s easy to scale horizontally. But agentic or reasoning-based inference involves multi-step interactions, persistent sessions, and orchestration across multiple services or agents. Now, the infrastructure must handle session affinity, cache management, and potentially orchestrate calls to external APIs or databases, all while maintaining low latency and high throughput.

Architectural implications:

• Stateless inference can be scaled with simple load balancing and replication, making it ideal for real-time AI applications like chatbots or voice assistants.
• Stateful/agentic inference demands session-aware routing, distributed caching, and orchestration layers.
• The network must support both high-throughput and low-latency, context-rich flows for advanced machine learning and deep learning models.
  
  

Infrastructure Implications: Flexibility Is Non-Negotiable

Industry leaders like Meta, Microsoft, and Google have converged on one principle: flexibility is non-negotiable. Their AI inference platforms aren’t static; they’re composable, able to reconfigure on the fly to meet shifting demand. Techniques like continuous batching, hierarchical caching, and deployment solvers optimize for both throughput and latency, while BGP EVPNs and modern network fabrics enable rapid adaptation across workloads and use cases.

Real-World Architectures: Lessons from the Hyperscalers

Meta’s Llama deployment required advanced parallelism techniques, specialized inference runtimes, and a focus on latency metrics like first token latency and streaming throughput. Their platform uses continuous batching (dynamically grouping requests), hierarchical caching to accelerate common queries, and custom deployment solvers to optimize model placement. Microsoft’s Azure AI infrastructure is modular, with VM types tailored for dense generative AI inferencing, high-speed interconnects, and optimized libraries. Google leverages Kubernetes-based deployments, auto-scaling, and global load balancing to deliver flexible inference at scale. NVIDIA’s Triton Inference Server and Dynamo framework enable dynamic scheduling and scaling across multi-GPU, multi-node environments, supporting both stateless and agentic workloads.

These platforms aren’t just technical marvels, they’re blueprints for adaptability. The key is composability: the ability to assign resources where they’re needed, when they’re needed, and to reconfigure the environment as workloads shift. This is the only way to keep pace with the relentless change in AI models, user demand, and business priorities.

The Role of BGP EVPNs and Composable Fabrics

So how do enterprises and GPU cloud providers achieve this kind of flexibility in their own data centers? Increasingly, the answer is BGP EVPN (Border Gateway Protocol Ethernet VPN), a technology that underpins many of the most advanced, adaptable AI fabrics in use today.

What is BGP EVPN? BGP EVPN is a modern control plane for data center networks, enabling scalable, multi-tenant Layer 2 and Layer 3 connectivity over an underlying IP fabric. In practical terms, it allows you to build virtual private clouds (VPCs) and segment your infrastructure flexibly, without being locked into static VLANs or rigid topologies. This is essential for supporting the scalability and automation required for modern AI applications and machine learning workloads.

Why does this matter for artificial intelligence and machine learning?

• Rapid reconfiguration: As AI workloads shift between training and inference, or as new models are deployed, BGP EVPN enables network segments to be created, modified, or removed on demand—without manual intervention or downtime.
• Multi-tenancy and isolation: Enterprises and service providers can securely run multiple projects, business units, or customers on the same physical infrastructure, each with their own isolated network segments and policies.
• Seamless scaling: Need to add more GPUs to a training cluster, or spin up a new inference environment for a different model? BGP EVPN abstracts the complexity, allowing resources to be pooled and allocated dynamically.
• Automation: Hedgehog's AI networking solution provides simple VPC API's to orchestrate networking services and utlizises kubernetes for fabric control, making it possible to define and deploy network topologies as code, in sync with the needs of your AI workloads.

Example scenario: Imagine an enterprise running both massive AI training jobs and latency-sensitive inference services on the same infrastructure. With BGP EVPN, the network can be programmatically reconfigured to allocate more bandwidth and lower-latency paths to the training cluster during the day, then shift resources to inference environments at night—without manual re-cabling or disruptive changes. This is the kind of agility that hyperscalers take for granted, and it’s increasingly within reach for enterprises adopting modern network fabrics. This flexibility is critical for supporting a wide variety of AI use cases, from real-time healthcare diagnostics to autonomous vehicles and generative AI applications.

The Evolving Reality: There Is No One-Size-Fits-All

It’s tempting to imagine a world where you have a cluster for training, another for inference, and you’re done. The reality is far more complex. Workloads shift daily, sometimes hourly. Some AI training jobs are massive, others are fine-tuning runs or specialized models. Some inference models are huge and require distributed serving; others are lightweight and ephemeral. Demand from users can spike unpredictably, and new models with new requirements are emerging constantly.

Why is this so challenging?

• Resource allocation: The optimal configuration for one workload may be wasteful or even harmful for another. Over-provisioning for peak demand leads to idle resources; under-provisioning causes bottlenecks and missed business opportunities.
• Operational overhead: Manual reconfiguration is slow, error-prone, and simply doesn’t scale in dynamic environments. Automation and programmability are essential.
• Business agility: The organizations that win are those that can launch new AI solutions and services, adapt to changing demand, and support innovation without being constrained by yesterday’s infrastructure decisions.

The best architectures are designed for change—capable of allocating resources to AI training one day, inference the next, and supporting everything from stateless microservices to sprawling agentic workflows. As artificial intelligence infrastructure evolves, the winners will be those who embrace complexity, design for flexibility, and build networks that empower—not constrain—their ambitions.

Practical Guidance: What Should Infrastructure Leaders Do?

1. Know your workloads. Invest in telemetry and profiling to understand the true demands of your AI training and inference jobs. Don’t assume inference is “easy” just because it was last year.
2. Design for flexibility. Use composable fabrics, BGP EVPNs, and automation to enable rapid reconfiguration. Avoid hard-coding your infrastructure to a single model or workload type.
3. Prioritize low-latency, high-bandwidth networking. Especially for large models and agentic inference, the backend network can become the bottleneck—just as with training.
4. Plan for both stateless and stateful inference. Build session-aware load balancers, cache management strategies, and orchestration layers that can handle long-lived, multi-step sessions and optimize user experience.
5. Stay up to date. The pace of change in AI infrastructure is relentless. Follow the latest research, case studies, and industry commentary—what worked last year may already be obsolete.
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Conclusion: Networks That Know Their Purpose

The distinction between AI training and inference isn’t merely academic—it has profound implications for how we design, build, and operate artificial intelligence infrastructure. Getting it wrong doesn’t just impact performance; it undermines the entire economic premise of your AI investment.

For infrastructure architects and technical leaders, the key takeaways are clear:

• Know your primary AI workload type and design your network architecture accordingly.
• If supporting both types, consider bifurcated or specialized designs rather than compromise solutions.
• Recognize that network requirements will evolve as your AI strategy matures from experimentation to production.
• Monitor the economic impact of network efficiency on your total cost of AI operations.
• Stay informed about emerging technologies like Ultra Ethernet that may eventually bridge the gap between AI training and inference requirements.

The fact that AI training and inference demand different network configurations isn’t a limitation to overcome, but a reality to embrace. By designing networks that know their purpose—whether facilitating the intensive east-west communication of distributed training or the responsive north-south flows of inference serving—we create infrastructure that enables rather than constrains our AI ambitions.

As we move forward into an era where artificial intelligence becomes increasingly central to business operations, the organizations that thrive will be those that recognize and account for these fundamental distinctions in their infrastructure planning. Your network needs to know the difference between AI training and inference—because your business success increasingly depends on it.

This post is the second in a comprehensive series exploring networking requirements for AI infrastructure and AI inferencing. In upcoming articles, we’ll dive deeper into specific technologies, implementation strategies, and architectural patterns that can help organizations overcome the limitations of traditional networks for machine learning and deep learning workloads.