Bridging the Gap: Traditional Infrastructure and AI Infrastructure

There is a growing sentiment within the engineering community that traditional infrastructure and AI infrastructure are fundamentally different domains. Many experienced engineers—experts in networking, compute, and storage—find that their established knowledge does not always translate directly to the rapidly evolving landscape of AI infrastructure. With the introduction of concepts such as GPUs, KV Cache, and 3D parallelism, it is understandable to feel out of depth, as if entering a new ecosystem.

This perception is common. The initial impression of AI infrastructure is often one of unfamiliarity, fragmentation, and a steep learning curve. However, is AI infrastructure truly a radical departure from established practices, or is it a natural evolution of infrastructure engineering?

My perspective: The gap is not as wide as it appears. AI infrastructure is a reconstruction and extension of traditional infrastructure, purpose-built for a new class of problems.

Comparing Traditional Infrastructure and AI Infrastructure

At first glance, the differences between traditional infrastructure and AI infrastructure are significant. Traditional infrastructure manages web requests, data storage, and distributed service coordination. In contrast, AI infrastructure—especially for large language models (LLMs)—focuses on GPU-accelerated inference, KV Cache management, and large-scale model training frameworks.

The request patterns also differ. A typical web request is stateless and completes in milliseconds. An LLM inference session, however, is stateful, can last several seconds or more, and must dynamically maintain context at the token level.

Even the technology stacks seem distinct. Traditional stacks might include Kubernetes and Docker, while AI infrastructure discussions often reference GPU, vLLM, DeepSpeed, FlashAttention, Triton, and NCCL—technologies that can appear complex and specialized.

Yet, beneath the surface, the core engineering challenges remain familiar.

Core Distributed Systems Concepts in AI Infrastructure

The fundamental challenges of any distributed system can be distilled into three enduring concepts:

• Scaling
• Sharding
• Replication

Scaling

In traditional infrastructure, scaling involves deploying additional servers or containers and using load balancers to distribute traffic.

In AI infrastructure, scaling is achieved through data, model, and pipeline parallelism, distributing workloads across multiple GPUs to train large models and serve high volumes of inference requests.

Sharding

In a database system, sharding partitions data across nodes to handle high-throughput operations.

In AI infrastructure, sharding involves dividing model parameters, KV Cache, activations, gradients, and optimizer states. Techniques such as tensor parallelism and paged attention are advanced forms of sharding, essential for distributed training and inference.

Replication

In traditional systems, replication ensures fault tolerance and performance, such as with database replicas or cache warming.

In AI infrastructure, replication is more complex and costly. For example, data parallelism requires copying the entire model to each GPU, prompting optimizations like ZeRO, which shards model states to reduce overhead. This relies on high-performance communication fabrics such as RDMA and NCCL.

The essence of the challenge remains: efficiently coordinating resources across machines. AI infrastructure introduces constraints such as limited GPU memory, large context windows, and billion-parameter models, making these challenges more acute and requiring sophisticated engineering solutions.

Case Study: vLLM as an Operating System for LLMs

Take vLLM as an example:

Think of it as a specialized operating system for LLMs. Its core function is to schedule "processes" (inference requests) and manage "memory pages" (the KV Cache), applying established OS memory management principles to the unique challenges of LLM serving.

The Importance of Latency Awareness in AI Infrastructure

Years ago, Google's Jeff Dean compiled the influential Key Numbers Every Programmer Should Know, emphasizing the importance of understanding latency in system design.

A strong intuition for these numbers enables engineers to anticipate bottlenecks, diagnose performance issues, and optimize systems effectively.

• Pre-Deployment Estimation: Estimate model training duration, expected inference throughput, and token latency using fundamental latency figures.
• Post-Deployment Diagnostics: Identify whether bottlenecks are due to communication, memory bandwidth, or compute limitations by understanding system latencies.

Case Study: During the training of Meta's LLaMA series, the team reportedly encountered GPU errors or task failures every few dozen minutes. This highlights the critical need for robust logging, error tracing, and profiling tools to ensure the stability of LLM training infrastructure.

Engineering Fundamentals Remain Central

A great infrastructure engineer not only builds functional systems but also traces latency end-to-end, deconstructs dependencies, and profiles for cost and performance. AI infrastructure, like traditional infrastructure, demands strong engineering fundamentals.

While LLMs introduce new workloads and resource constraints—particularly GPU memory and interconnect bandwidth—the core engineering principles remain unchanged: maximize resource utilization, ensure stability, and optimize throughput and latency.

Mapping Traditional Skills to AI Infrastructure

The barrier to entry for AI infrastructure is real, but it is not about expertise in neural networks. Success depends on translating existing engineering skills and system design thinking to a new domain.

• Experience with high-performance networking provides insight into NCCL's ring all-reduce for cluster communication.
• Understanding OS paging and caching clarifies the importance of the KV Cache and its efficient management.
• Familiarity with service schedulers makes concepts like dynamic batching recognizable as forms of pipeline concurrency.

Conclusion: Connecting Traditional and AI Infrastructure

In summary, while AI infrastructure introduces new challenges and technologies, the foundational engineering principles remain consistent with those of traditional infrastructure. By mapping established concepts to this new domain, engineers can leverage their existing expertise to build robust, efficient, and scalable AI systems. The connection between traditional and AI infrastructure is not only relevant—it is essential.