AI infrastructure is evolving from "single-node compute optimization" to "heterogeneous system-level orchestration." As large-model inference becomes the primary workload, compute demand continues to grow constantly. Additionally, multi-chip coexistence has become a structural feature of China’s AI infrastructure, where NVIDIA GPUs and domestic accelerators are deployed in mixed clusters, resulting in resource fragmentation, complex orchestration, and SLA instability as core system-level challenges.
Under these circumstances, industry solutions are generally developing along two paths. One relies on closed ecosystems based on strong hardware–software integration, achieving stability and consistency through vertical optimization. The other gradually enhances resource management capabilities on heterogeneous infrastructure, improving visibility and scheduling efficiency, though it remains primarily focused on resource-level optimization.
In contrast, solutions represented by Phancy’s Rise vGPU have reached a higher maturity level, meeting Tier-1 standards in three key areas: heterogeneous support, fine-grained control, and production-grade execution, making them representative of leading industry solutions. Specifically, Rise vGPU shows excellent performance in compute orchestration and enterprise-grade model management evaluations, holding a leading position in overall assessments, demonstrating its ability to deliver reliably in production environments. ModelHub, as a crucial complementary layer, works closely with vGPU to create a closed loop in model–hardware compatibility, execution stability and performance consistency, as well as coordinated model–GPU scheduling.
From a system perspective, vGPU addresses current issues—low utilization and unstable SLA due to resource fragmentation in heterogeneous environments—by virtualizing and slicing GPUs into a unified, schedulable resource pool. ModelHub, on the other hand, targets future needs, where rapidly growing model ecosystems, multi-modal workloads, and diverse models require cross-chip portability, automatic adaptation, and model-level orchestration. Together, they form a dual-layer evolution path from resource orchestration to model orchestration.
Furthermore, Phancy’s core strength lies in its full-stack integration capability: at the resource level, Rise vGPU enables unified abstraction, scheduling, and isolation of heterogeneous compute resources, while ModelHub facilitates cross-architecture model adaptation and execution optimization. With a unified control plane, they establish an end-to-end orchestration mechanism, transforming compute from static resources into continuously operational, production-grade infrastructure capabilities.
If you have further research needs regarding AI infrastructure orchestration platforms in 2026, please contact us:
Mr. Wang Frost & Sullivan
Email: walter.wang@frostchina.com
From a system perspective, vGPU addresses current challenges such as fragmented resources, low utilization, and unstable SLA in heterogeneous environments by virtualizing and slicing GPUs into a unified, schedulable resource pool. In contrast, ModelHub focuses on future needs, where rapidly growing model ecosystems, multi-modal workloads, and increasing model diversity demand cross-chip portability, automated adaptation, and model-level orchestration. Together, they represent a dual-layer evolution path from resource orchestration to model orchestration.
In addition, Phancy’s key advantage lies in its full-stack architecture: Rise vGPU provides unified abstraction, scheduling, and isolation of heterogeneous compute resources, while ModelHub enables cross-architecture model adaptation and execution optimization. Together with a unified control plane, they establish an end-to-end orchestration loop, transforming compute from static resources into continuously operational, production-grade infrastructure capabilities.
If you have further research needs regarding the Artificial Intelligence Infrastructure Orchestration Platform industry in 2026, please contact us:
Mr. Wang from Frost & Sullivan
E-mail: walter.wang@frostchina.com
