Maximize AI Infrastructure Throughput by Consolidating Underutilized GPU Workloads

A new performance benchmark demonstrates that hardware-level GPU partitioning is a more effective strategy than software-based sharing for maximizing throughput in production AI systems. The study, which tested a multi-model voice AI pipeline, found that NVIDIA's Multi-Instance GPU (MIG) technology handled significantly higher request volumes compared to software time-slicing. This finding is critical for organizations looking to reduce operational costs and improve efficiency, as it provides a clear path to consolidating lighter workloads like speech recognition and synthesis onto a single GPU without compromising performance, freeing up valuable hardware for more demanding large language models.

The research addresses the common problem of GPU underutilization in Kubernetes, where lightweight support models often occupy entire high-cost processors. By testing on NVIDIA A100 GPUs, the experiment compared two consolidation methods: software-based time-slicing, which interleaves processes but lacks hardware isolation, and MIG, which physically partitions a GPU into isolated instances. Under a heavy load of 50 concurrent users, the MIG configuration achieved a throughput of approximately 1.00 request per second per GPU, while the time-slicing setup reached only 0.76 req/s. The performance drop in time-slicing was attributed to scheduling overhead from context switching between different AI models.

These results offer a practical decision-making framework for MLOps and infrastructure teams. For production environments with enterprise-level service agreements, MIG is the recommended approach due to its superior throughput and fault isolation, which prevents a single model's error from affecting others on the same card. Software-based time-slicing, however, remains a viable option for development, testing, or low-concurrency applications where maximizing hardware density for proofs-of-concept is more important than achieving peak performance and reliability. This distinction helps organizations align their infrastructure strategy with specific business and operational requirements.

For production AI services with strict uptime and throughput requirements, the data indicates that the hardware isolation provided by NVIDIA MIG offers a more reliable path to maximizing infrastructure ROI than the flexibility of software-based sharing like time-slicing.