From chips to chains: How AI hardware is redefining System Design
System Design has traditionally focused on software, databases, and networking, with hardware treated as a commoditized afterthought, especially in cloud environments. Today, the availability, cost, and capability of AI hardware have become primary constraints, driven by intense demand for high-end GPUs, supply chain disruptions, and geopolitical restrictions. As a result, engineers can no longer assume abundant, uniform compute and must design systems that are resilient to scarcity, aware of heterogeneous resources, and optimized around specific hardware. This shift is forcing companies to redesign training and inference pipelines and pushing system architects to think more like hardware engineers, fundamentally reworking architectures rather than simply optimizing code.
This newsletter explores how these hardware trends are reshaping AI System Design. It covers the following topics.
The strategic shift toward custom silicon.
Architecting systems for heterogeneous compute environments.
The complexities of scheduling and cluster design at a massive scale.
Long-term strategies for navigating a volatile hardware ecosystem.
To understand how these forces are reshaping AI infrastructure, we must examine the fundamental transformation of the relationship between hardware and software architecture that has occurred in recent years.
Hardware as a System Design challenge#
The traditional model of System Design treated compute as a fungible resource. You would design your software architecture and then provision the necessary CPUs or GPUs. This paradigm has been inverted, and now access to accelerators dictates architectural choices from the outset.
GPU scarcity has become a core business and technical constraint. Lead times for top-tier GPUs can stretch for months, and their costs have increased dramatically. These factors make large-scale deployments a significant financial risk. This pressure forces teams to make difficult tradeoffs. For example, a company with a large investment in H100 GPUs must design its MLOps pipeline to maximize its utilization, even if that requires changes to system architecture or execution logic. We see industry adaptation in multiple ways.
The following illustration shows the key factors driving this hardware-centric shift and their downstream effects on System Design decisions.
Some companies are delaying AI projects, while others are pivoting to less powerful but more readily available hardware, accepting performance penalties. This new reality requires a proactive, hardware-aware approach to System Design. The chip supply chain is now as critical as the software supply chain.
Understanding these pressures explains the first major trend in system adaptation: the shift away from general-purpose hardware.
The rise of custom silicon in large-scale systems#
In response to the limitations of general-purpose GPUs, major tech organizations are investing billions to develop their own
A general-purpose GPU, such as an H100, is versatile, but a custom accelerator is specialized. By removing unnecessary components and optimizing data paths for specific model architectures, these chips can deliver superior performance for targeted tasks. For instance, chips designed for inference prioritize low-latency processing, which is a different goal from the high-throughput processing needed for training.
The architectural differences between systems built on general-purpose vs. custom hardware are significant, as illustrated in the following diagram.
Key difference: In the above illustration, both architectures have distinct build-time and runtime phases. General-purpose GPUs use standard ML frameworks during build-time, while custom accelerators require specialized compilation services (like AWS Neuron SDK) to optimize models for specific hardware. At runtime, both connect to the CPU via PCIe, but custom silicon delivers superior performance for targeted workloads.
A prime example is Amazon’s strategy around AWS Inferentia. The company designed these chips specifically to lower the cost of running machine learning inference at scale. Systems built around Inferentia are architected differently. They often utilize a just-in-time (JIT) compilation service called Neuron, which compiles PyTorch or TensorFlow models into optimized code that runs efficiently on the Inferentia hardware. This requires architects to integrate the Neuron SDK into their deployment pipelines, a design choice explicitly driven by the custom hardware.
Custom silicon in AI inference: Some companies report that moving large-scale inference workloads to TPUs delivers notable cost and efficiency gains compared to GPU-centric setups. Achieving this often requires re-architecting the inference service. For example,
Most organizations will not use only one type of hardware. This introduces the challenge of managing diverse compute environments.
Designing systems for heterogeneous computing#
The proliferation of custom silicon alongside traditional CPUs and GPUs means that modern AI systems must operate in a
Effective orchestration means partitioning a single AI workload and assigning different parts to the hardware best suited for them. For instance, in a recommendation system, the complex embedding lookups, which are often memory-bound and may benefit from large-capacity CPU memory or specialized accelerator memory hierarchies, can be offloaded to CPUs. Meanwhile, the dense matrix multiplications of a deep learning model can be sent to a GPU or a custom accelerator for execution. This division of labor prevents a single, expensive accelerator from being tied up with tasks for which it is not optimized.
This diagram shows how different stages of an AI pipeline can be mapped to various hardware components.
This strategy improves hardware utilization and energy efficiency. CPUs are more power-efficient for sequential, logic-heavy tasks than GPUs. Reserving accelerator time for parallelizable, computationally intensive work reduces overall power consumption and operational costs. The goal is to create an efficient system where each component is used for its optimal task.
Assigning these tasks requires an intelligent control plane, which relates to the complexities of workload scheduling.
Workload placement and scheduling complexity#
In a multi-hardware environment, the scheduler acts as the control plane. A modern AI system-level scheduler must be hardware-aware. It needs to understand the capabilities, constraints, and costs associated with each processor in the cluster. This intelligence is crucial for meeting performance targets and maximizing resource utilization.
For example, a scheduler might automatically route a large-scale model training job, which requires high throughput and massive parallelism, to a cluster of interconnected H100 GPUs. In contrast, it would direct a latency-sensitive inference request for a small model to a single, power-efficient custom accelerator, such as an AWS Inferentia chip. To achieve this, the scheduler must consider various factors, including data locality, network bandwidth between nodes, power consumption, and job priority. This is a complex, multidimensional optimization problem that sophisticated platforms, such as Kubernetes with NVIDIA device plugins or custom-built schedulers at hyperscalers, aim to solve.
Effective scheduling has a direct impact on system efficiency and cost. A poorly routed job can underutilize expensive hardware or fail to meet its service-level objectives (SLOs). Therefore, designing a robust scheduling subsystem is a critical aspect of building scalable AI infrastructure today.
The following table summarizes the key characteristics of different scheduler designs for AI workloads.
Scheduler Type | Hardware Awareness | Key Features | Target Use Case |
Basic FIFO scheduler | Low | Simple queuing | Development environments |
Capacity scheduler | Moderate | Resource-pool based, handles quotas, priorities | Multi-tenant clusters |
Hardware-aware orchestrator (e.g., Kubernetes with plugins) | High | Topology-aware routing, heterogeneous hardware support | Production-scale training and inference |
As clusters grow from dozens to tens of thousands of accelerators, the architectural challenges multiply, requiring entirely new design patterns.
Cluster design at massive accelerator scale#
Building an AI cluster with over 20,000 GPUs introduces new challenges, as traditional data center assumptions no longer apply. At this scale, node failures occur multiple times a day. Systems must be designed for resilience and elasticity.
Training jobs must be able to handle the loss of a node without requiring a restart from scratch. This concept is known as
To manage this complexity, hyperscalers like
In a large H100 deployment, a training job for a foundation model can lose a few nodes, automatically re-provision replacements, and continue from the last checkpoint. This level of automation and fault tolerance is essential for ensuring productivity and return on investment on such massive hardware deployments.
The following diagram provides a high-level view of how these massive clusters are architected for resilience.
The network fabric that connects these nodes is often the most critical component and a common performance bottleneck.
Network and interconnect as a system bottleneck#
In distributed AI systems, network performance is as critical as compute performance. As models grow, training becomes a parallel task requiring continuous state synchronization across accelerators. At this scale, interconnect bandwidth and latency become the primary performance limiters. Traditional or poorly optimized Ethernet deployments often lack the bandwidth or latency characteristics required for these communication patterns.
High-performance interconnects, such as NVIDIA’s NVLink and InfiniBand, address this requirement. These technologies provide high-speed communication paths between GPUs. The performance of collective communication operations like all-reduce (a collective communication operation that aggregates data from all nodes and distributes the result back to all nodes) is directly tied to the interconnect’s performance. A slow network can leave expensive GPUs idle, creating a system bottleneck.
The diagram illustrates how traditional Ethernet creates a communication bottleneck in distributed AI systems, whereas high-performance interconnects like NVLink and InfiniBand enable efficient parallel processing.
Modern System Design for large-scale AI involves co-designing the compute and network architectures. This includes planning the network topology to ensure non-blocking communication and tuning software to maximize bandwidth utilization. Mitigating network bottlenecks is a primary focus for performance engineers.
Using specialized hardware and high-performance networks effectively requires significant adaptations in the software layer.
Software architecture adaptations for specialized hardware#
For emerging custom accelerators, hardware innovation currently outpaces the maturity of the surrounding software ecosystem. Extracting maximum performance from specialized accelerators requires adapting the software architecture. This has driven evolution in machine learning frameworks, compilers, and distributed training algorithms. Frameworks like PyTorch and TensorFlow are introducing new abstractions to target diverse hardware backends, including those beyond NVIDIA GPUs.
We are seeing a rise in hardware-aware compilers, such as Google’s XLA and AWS’s Neuron, which take high-level model code and generate optimized machine code for specific chips. These compilers can perform hardware-specific optimizations, such as instruction scheduling and memory layout changes, that are invisible to the data scientist. Additionally, new model parallelism strategies (e.g., tensor, pipeline, and sequence parallelism) have been developed specifically to train massive models that cannot fit on a single accelerator, requiring sophisticated software to orchestrate the computation across many devices.
A best practice for System Designers is to build an abstraction layer that isolates application logic from hardware specifics. This approach retains flexibility and avoids hardware lock-in. It also allows performance engineers to optimize for specific targets when necessary.
This table provides a high-level comparison of software stacks and their support for different AI accelerators.
Software Framework | Supported Accelerators | Key Strengths |
PyTorch | NVIDIA GPUs, Google TPUs, AWS Trainium, AWS Inferentia | Flexibility, strong community |
TensorFlow | NVIDIA GPUs, Google TPUs | Mature ecosystem, XLA compiler |
Vendor-Specific Stacks (e.g., NVIDIA CUDA, AWS Neuron) | Tied to specific hardware | Highest possible performance |
Navigating these software and hardware choices requires a long-term strategic vision, especially given the current geopolitical climate.
Long-term System Design trade-offs in the chip war#
System architects now operate amid intense geopolitical competition over semiconductor technology. This introduces uncertainty and risk into designs. Export controls can restrict access to the latest hardware, while supply chain disruptions can delay deployments. Relying on a single hardware vendor poses both technical and significant business risks.
The primary long-term strategy to mitigate this is to design for portability by building abstraction layers that decouple AI applications from the underlying hardware. This may involve using open standards, such as
The cost of abstraction: While abstraction layers provide flexibility, they come at a cost. They can introduce performance overhead and may not expose all the specialized features of the underlying hardware. Teams must carefully evaluate this tradeoff based on their risk tolerance and performance requirements.
A real-world example is maintaining parallel development tracks to validate models on both NVIDIA GPUs and an alternative accelerator. This approach requires additional engineering effort but mitigates supply chain risk. The tradeoff is between short-term performance and long-term strategic resilience, with resilience becoming increasingly important.
These strategic considerations ultimately shape how organizations should approach AI infrastructure planning moving forward.
Building for a multipolar hardware future#
Hardware can no longer be treated as a simple commodity. System Design for AI now requires navigating hardware constraints, opportunities, and risks. Every architectural decision, from using custom silicon to designing large-scale clusters, is intricately intertwined with the silicon on which it will run.
The primary takeaway is the need for flexibility. Systems should be designed to be performant, portable, and resilient. Building effective abstraction layers and planning for a multi-vendor hardware world helps create AI systems that are powerful and adaptable to future changes.
