Guide Outlines On-Premise 'GPU-as-a-Service'
A detailed guide architects a "GPU-as-a-Service" (GPUaaS) model for on-premise enterprise AI. Key patterns include using Kubernetes for multi-tenant scheduling to isolate workloads while maximizing GPU utilization. The guide also emphasizes the need for dynamic cost modeling for chargebacks and fine-grained RBAC and quotas to manage resources.
- Research indicates a breakeven point for on-premise GPU infrastructure versus cloud at around 33% utilization; below this, cloud services are more economical, but consistent, high-utilization workloads can see significant cost savings with an on-prem model. - Native Kubernetes can only assign whole GPUs to individual pods, which leads to underutilization. To overcome this, NVIDIA's Multi-Instance GPU (MIG) technology partitions a single GPU into multiple, isolated hardware instances, each with its own memory and compute resources. - The GPU orchestration space has seen significant consolidation, highlighted by NVIDIA's acquisition of Run:ai, a company that developed a virtualization and orchestration platform to manage GPU resources more efficiently on Kubernetes. - Implementing a GPUaaS platform involves more than just hardware; the NVIDIA AI Enterprise software suite provides a full stack for managing AI workloads, including certified drivers, Kubernetes operators for GPUs and networking, and various AI frameworks. - A key challenge in multi-tenant GPU environments is the lack of strong hardware isolation by default, as GPU device drivers operate in a privileged mode, and long-running AI workloads can amplify the impact of failures across different tenants. - The total cost of ownership for on-premise GPU clusters extends beyond the hardware to include substantial expenses for power, cooling, and high-speed networking like InfiniBand, which is critical for efficient multi-GPU training. - For inference workloads, achieving high utilization is critical to lowering the cost-per-token. A platform running at 80% utilization can produce tokens at nearly half the unit cost of one at 40%, making efficient scheduling a key factor in the economic viability of a service.
