What Does Multi-tenancy Mean in Relation to HPC and Warewulf

https://www.hpcwire.com/2025/07/24/multi-tenant-hpc-and-ai-how-the-network-can-make-or-break-the-system/

As artificial intelligence (AI) and HPC applications rapidly evolve and diversify, organizations are rethinking how to design and scale the infrastructure that powers these systems. Central to this transformation is the concept of multi-tenant AI clusters. These environments allow multiple organizations or teams to share a common compute fabric while running heterogeneous AI and HPC workloads. Whether it’s training foundation models, processing edge analytics, running traditional HPC MODSIM applications, or running digital twin simulations, these clusters must juggle conflicting demands on performance, scalability, and reliability.

At the heart of this infrastructure lies the network. While compute and storage resources are essential, it is the network that binds them into a cohesive system. In multi-tenant AI environments, the network can either be a powerful enabler of efficiency or a significant bottleneck that limits scalability and performance. This article examines the fundamental networking challenges in multi-tenant HPC-AI clusters and how different networking technologies measure up. Core Networking Challenges of Multi-Tenant AI

A network switch on the cluster. Image courtesy of Karpathy/Tesla.

Resource isolation: Tenants must be insulated from each other to ensure that one user’s heavy workload does not degrade the performance of others. This requirement demands strict traffic separation and predictable performance across the network fabric.
Network load balancing across tenants: Dynamic traffic patterns mean the network must intelligently balance loads to avoid hotspots and ensure fairness.
Multi-tenancy without overlay overhead: Network overlays (e.g., VXLAN) are often used for tenant isolation but add significant overhead and complexity, especially at scale. A native solution is preferable.
Full fabric utilisation regardless of frame size: AI workloads generate a mix of large (e.g., model checkpoints) and small (e.g., parameter updates) packets. The network must sustain high utilisation across all traffic profiles.
Full utilisation of all traffic types: Whether it’s RDMA over Converged Ethernet (RoCE), TCP, or proprietary protocols, the network must provide consistent performance.
Peak performance even under worst node allocation: In shared clusters, tenants might be scheduled on physically distant or suboptimal nodes. The network must sustain peak performance even in these less-than-ideal scenarios.
Seamless recovery in case of failure: HPC and AI training jobs often span days or weeks. As any network hiccup can be costly, resiliency and fast failover are essential.
Dynamic scaling across nodes and sites: As demand grows, clusters expand across racks, rows, and even geographies. The network must scale seamlessly to support this elasticity. Comparing Networking Technologies InfiniBand

Strengths: InfiniBand offers very low latency and high throughput, making it a natural fit for performance-sensitive AI workloads. It also features native support for RDMA, which enables efficient data movement with minimal CPU overhead.

Limitations: Despite its strengths, InfiniBand is part of a proprietary ecosystem. It is controlled by a single vendor, limiting flexibility and interoperability. Resource isolation is a complex process that often relies on software layers at the host level. Furthermore, scaling InfiniBand beyond a single data centre or vendor-specific design is a significant challenge. Failures in core switches can impact a wide part of the infrastructure due to large failure domains. Standard Ethernet

Strengths: Standard Ethernet is a widely used, cost-effective, and well-supported protocol across various hardware and software ecosystems. It provides a familiar and interoperable foundation for building large-scale networks.

Limitations: Standard Ethernet struggles to deliver predictable performance under AI workloads. Congestion and packet loss are common, which undermines the latency requirements of AI systems. To achieve RoCE performance, administrators must engage in complex tuning of flow control, ECN, and QoS mechanisms. Furthermore, network overlays are often required to implement tenant isolation, which adds overhead and complicates troubleshooting. Endpoint-Scheduled Ethernet

Strengths: Endpoint-scheduled Ethernet introduces traffic orchestration at the server level, enhancing predictability and performance. It can be effective in tightly controlled environments where coordination between the compute and network layers is feasible.

Limitations: This approach introduces considerable coordination complexity. It relies on a tightly coupled relationship between high-end, expensive network interface cards (NICs) and the network, which makes it difficult to scale. As the number of tenants and nodes increases, so does the overhead of managing this coordination. Additionally, traffic scheduling often assumes a level of trust between workloads that may not be appropriate in multi-tenant environments. Fabric-Scheduled Ethernet

Strengths: Fabric-scheduled Ethernet takes a fundamentally different approach by moving traffic scheduling into the network fabric itself. This architecture ensures deterministic, lossless performance for both RoCE and TCP traffic. It supports full tenant isolation using multiple virtual lanes without relying on overlays. The built-in intelligence of the fabric enables optimal bandwidth utilisation across different traffic types and frame sizes. With path diversity and fast failover recovery mechanisms, it also ensures high availability and supports seamless scaling across racks, rows, and even sites.

Limitations: Although fabric-scheduled Ethernet offers a comprehensive solution, its implementation requires the use of modern fabric switches. Which Network Architectures Best Fit Multi-Tenant HPC-AI?

While all of these technologies offer resource isolation functionality, there are two different concepts. The first concept includes advanced functionalities such as partition keys, virtual lanes, and congestion control mechanisms; however, these functionalities struggle to offer optimal isolation and come with the overhead of additional payload and increased configuration complexity. The second concept offered by fabric-scheduled Ethernet includes inherent isolation functionality, thanks to multiple egress virtual queues resulting in isolation without overheads.

Load balancing mechanisms are constantly evolving due to the importance of functionality and their impact on the AI cluster’s bandwidth. An innovative concept of cutting packets into small, unified cells and then spraying them over all available network links offers the best load balancing, solving elephant flow issues, while requiring no configuration or tuning, even when workloads change. Cell spraying is currently offered solely through fabric-scheduled Ethernet technology.

Fabric-scheduled Ethernet architecture offers superior functionality at the key points, resulting in the following key benefits:

Deterministic, lossless fabric: supporting both RoCE and TCP with congestion-aware, lossless forwarding across the entire fabric
Native multi-tenancy: delivering strict isolation without overlays or excessive tuning through segmentation and isolation at the fabric level
Full-fabric utilisation: natively maximising throughput and efficiency for any frame size and workload type
High availability and resilience: protecting against failures through built-in redundancy and fast HW based convergence

Why the Network Defines HPC-AI Cluster Success

In traditional computing environments, the network often plays a secondary role. But in AI workloads, where thousands of GPUs or accelerators must synchronise and communicate in real-time, the network fabric is mission-critical.

Consider the impact of even small delays: a 1-2% slowdown in inter-node communication during deep learning training can translate to hours of lost compute time. When multiplied across dozens of jobs and thousands of nodes, these inefficiencies compound rapidly.

In a multi-tenant setting, these stakes are even higher. Poor isolation can lead to noisy-neighbour issues, where one workload disrupts others. Network congestion can become systemic, creating cascading delays. And failure to scale smoothly means AI innovation is constrained not by algorithms, but by infrastructure.

Analyzing current networking technologies regarding tenant isolation highlights the strong advantages of fabric-scheduled Ethernet, which appears to have been specifically designed to offer the necessary functionality: perfect load balancing, isolation without overlay overheads, Full fabric utilisation regardless of frame size or traffic type, and peak performance even under the worst node allocation. Conclusion: Networking is the AI Enabler

As AI becomes more pervasive and multi-tenant (HPC) clusters become the norm, the importance of a robust, intelligent, and scalable network fabric cannot be overstated. While traditional technologies like InfiniBand and standard Ethernet have served HPC-AI workloads in the past, they fall short in meeting the demands of modern, multi-tenant environments.

Fabric-scheduled Ethernet stands out as the next-generation solution, enabling resource isolation, high utilisation, and seamless scalability. With fabric-scheduled Ethernet technology, organizations can unlock the full potential of multi-tenant HPC-AI infrastructure, ensuring their networks become a catalyst—rather than a constraint—for innovation.