Scale-Up vs. Scale-Out in AI Infrastructure
Nov 05, 20251 min read
As AI models grow in size and complexity, data centers face unprecedented bandwidth, latency, and scalability challenges. Massive GPU clusters require networks capable of handling intensive east-west traffic and lossless data exchange. To meet these demands, AI infrastructure has evolved along two main scaling paths: scale-up, which boosts performance by enhancing computing power within a single node, and scale-out, which expands capacity by interconnecting multiple nodes through high-speed networks. Understanding how these two approaches differ is crucial for building efficient, future-ready AI infrastructures.
What Is Scale-Up Architecture?
Scale-up focuses on vertical enhancement—adding more GPUs, CPUs, or memory within a single server or node. Instead of connecting many small nodes, this approach builds a more powerful standalone computing system capable of handling intensive workloads internally.
In AI workloads, scale-up systems often appear in GPU servers or AI supernodes, equipped with high-speed interconnects such as NVIDIA NVLink, NVSwitch, or PCIe Gen5. These interconnects deliver massive bandwidth and low latency between GPUs, ideal for model parallelism and shared memory operations. The following are the benefits of scale-up architecture:
Ultra-Low Latency: Increasing single-node resources can reduce cross-node delay and improve internal communication efficiency.
Simplified Management: Fewer nodes reduce the complexity of cluster orchestration and fault management.
High Efficiency for Model Parallelism: Best suited for large-scale models that cannot easily be split across multiple nodes.
Optimized Hardware Utilization: Shared memory and compute resources improve performance per node.
What Is Scale-Out Architecture?
Scale-out refers to the horizontal expansion of an infrastructure by adding more nodes—servers, storage units, or network devices—to the cluster. Each node contributes additional computing or storage resources, forming a distributed system where workloads are shared.
In AI infrastructure, scale-out is typically used in large-scale GPU clusters or distributed AI training environments. Frameworks such as TensorFlow and PyTorch are designed to leverage this model, where workloads are split and processed across many GPUs connected by a high-bandwidth network. The following are the benefits of scale-out architecture:
High Scalability: Add more servers to increase capacity without major hardware changes.
Fault Tolerance: The system remains operational even if individual nodes fail.
Flexibility: Suitable for workloads that can be parallelized, such as distributed training or inference across large datasets.
Cost Efficiency: Easier to start small and scale incrementally based on demand.
Quick Overview: Scale-Up vs. Scale-Out
The core distinction lies in how they expand computational resources:
Scale-up strengthens a single node by increasing its internal resources.
Scale-out adds multiple nodes to distribute the workload.
Dimension | scale-up (Vertical) | scale-out (Horizontal) |
Expansion Method | Add more GPUs, CPUs, or memory within a single node | Add multiple nodes to expand resources horizontally |
Performance | High bandwidth and ultra-low latency within one system | Aggregate performance across nodes via network interconnects |
Scalability | Limited by physical hardware capacity and thermal constraints | Virtually unlimited, depending on network fabric design |
Resilience | Single point of failure—node failure may impact the workload | High fault tolerance—other nodes continue operating if one fails |
Cost | Higher upfront cost per node due to premium hardware integration | Lower initial cost; scales gradually with additional nodes |
Deployment Complexity | Easier to manage fewer, more powerful nodes | Requires sophisticated orchestration and traffic management across clusters |
Network Requirement | Intra-node interconnects such as NVLink, NVSwitch, PCIe Gen5 | High-speed Ethernet or InfiniBand with RoCEv2 for inter-node communication |
Use Cases | Model parallelism, real-time inference, small-to-medium AI workloads | Data parallelism, large-scale training, and distributed inferencing |
How Scale-Up and Scale-Out Architectures Work Together
In modern AI infrastructure, scale-up and scale-out are no longer competing strategies—they have become complementary layers of a unified, hierarchical architecture. By combining the strengths of both approaches, AI data centers can achieve higher compute density, greater scalability, and more efficient resource utilization.
At the intra-node level, the scale-up model enables tight coupling between GPUs, CPUs, and memory through high-speed interconnects like NVIDIA NVLink, NVSwitch, or PCIe Gen5. This ensures that parallel processes such as model partitioning or tensor synchronization occur with minimal latency. A single AI server can deliver petaflops of compute performance, forming a “super node” optimized for intensive training workloads.
At the inter-node level, the scale-out model connects these high-performance nodes through a low-latency lossless fabric. This allows data and gradients to be exchanged efficiently across thousands of GPUs during distributed training. Technologies like RoCE, ECN＆PFC congestion control, and adaptive load balancing ensure that network bandwidth scales linearly with cluster size.
For enterprises building AI-ready data centers, this hybrid coordination is critical. It allows workloads to move fluidly between nodes, minimizes bottlenecks, and delivers both the computational efficiency of scale-up and the horizontal flexibility of scale-out—a balance that defines the future of scalable AI networking.
How FS Solution Enables Scale-Up & Scale-Out Synergy
Built on H200 GPU servers, FS 800G RoCE lossless network solution seamlessly integrates both scale-up and scale-out architectures to deliver a high-bandwidth, low-latency infrastructure foundation optimized for intensive AI training workloads. Additionally, AmpCon-DC Management Platform enables real-time telemetry for performance monitoring, supports automatic topology discovery, and offers end-to-end RoCE EasyDeploy for one-click configuration of lossless networks.
Scale-Up: High-Bandwidth Intra-Node Connectivity
Within the architecture, each H200 GPU server node connects to the backend network built on N9600-640D(or N8650-32OD) 800G AI switches through 400G/800G links. This design enables fast gradient and parameter synchronization among GPUs, forming strong vertical compute aggregation capabilities. The scale-up layer ensures ultra-low latency communication within a node or localized GPU cluster, providing a lossless data path for intensive AI workloads.
Model | Chip | Rate | Capacity | Form Factor | Ports |
 N9600-64OD | BCM78900 Tomahawk 5 | 800G | 51.2 Tbps | 2U | 64 x 800/400/200/100GbE OSFP, Use Breakout for 128× 400GbE, or 256× 200/100GbE |
 N8650-32OD | BCM78902 Tomahawk 5 | 800G | 25.6 Tbps | 1U | 32 x 800/400/200/100GbE OSFP, Use Breakout for 64x 400GbE, 128x 200GbE or 256x 100GbE |
Scale-Out: Cluster-Level Horizontal Expansion
As training workloads grow, multiple GPU nodes must communicate across clusters through the frontend and storage networks. FS leverages N8550 and N9550 series switches to form a hierarchical spine-leaf fabric using 100G and 400G links, connecting compute and storage resources. This structure supports distributed AI training across hundreds or even thousands of servers, achieving efficient Scale-Out expansion at the cluster level.
Conclusion
Combining Scale-Up and Scale-Out strategies has become essential for building high-performance, future-proof AI infrastructure as AI models continue to grow in scale and complexity. FS AI data center solutions enable organizations to rapidly deploy flexible, high-capacity AI training and inference networks that are easy to manage, even with limited IT resources. By integrating a comprehensive, industry-leading hardware portfolio with the AmpCon-DC management platform, FS helps customers maximize GPU utilization, accelerate job completion times, and efficiently scale their networks. Visit our official website fs.com to explore lossless AI solutions, and contact us to design a stable, reliable, and fully customized data center network for your needs.