AI基础设施正在从“单点算力优化”向“异构系统级协同”演进。随着大模型推理成为核心负载，算力需求持续在线化增长，同时多芯片并存已成为中国AI基础设施的结构性特征，不同GPU与国产加速器在同一集群中混合部署，使资源碎片化、调度复杂化与SLA不稳定成为当前阶段的核心矛盾。
在此背景下，行业解决方案大致沿两条路径演进：一类依托云与硬件生态的封闭式体系，通过软硬件深度绑定实现稳定性与一致性；另一类在既有异构基础设施之上逐步增强资源管理能力，通过提升可视化与调度能力改善局部效率，但整体仍以“资源管理优化”为主。
相比之下，以范式（Phancy）的Rise vGPU为代表的方案已进入更高成熟度阶段，在“异构支持、细粒度控制、生产级执行”三大维度达到Tier-1准入标准，并成为当前领先梯队的代表性实现路径。其中，Rise vGPU在算力编排与企业级模型管理平台评估中表现突出，在综合评分中位居领先位置，体现其在生产环境中的稳定交付能力。ModelHub作为其关键互补层，与vGPU形成深度协同，在模型与芯片兼容性、执行稳定性与性能一致性、以及Model-GPU协同调度等关键环节构建系统闭环。
从架构视角看，vGPU解决的是“当前问题”——在异构环境下因资源碎片化导致的利用率低与SLA不稳定，通过虚拟化与细粒度切分将GPU转化为可调度资源池；而ModelHub面向的是“未来问题”——随着模型规模指数级增长与多模态/多任务模型生态形成，模型数量与运行形态显著扩展，需要跨芯片一致运行、自动适配与模型级调度能力。二者共同构成从资源到模型的双层基础设施演进路径。
在此之上，范式的核心优势在于其全栈一体化能力：在资源层通过Rise vGPU实现异构算力统一抽象、调度与隔离，在模型层通过ModelHub实现跨架构模型适配与运行优化，并通过统一控制平面实现端到端协同，使算力从“可用资源”进一步升级为“可持续运营的生产级基础设施能力”。
AI infrastructure is shifting from “single-node compute optimization” toward “heterogeneous system-level orchestration.” As large-model inference becomes the dominant workload, compute demand is increasingly always-on. Meanwhile, multi-chip coexistence has become a structural characteristic of China’s AI infrastructure, where NVIDIA GPUs and domestic accelerators are deployed in mixed clusters, leading to fragmentation, orchestration complexity, and SLA instability as core system-level challenges.
Against this backdrop, solution paths are broadly evolving along two directions. One relies on closed ecosystems built on tight hardware–software integration, achieving stability and consistency through vertical optimization. The other incrementally enhances resource management capabilities on top of heterogeneous infrastructure, improving visibility and scheduling efficiency, but remaining largely within the scope of resource-level optimization.
In contrast, solutions represented by Phancy (Rise vGPU) have entered a higher maturity tier, meeting Tier-1 standards across three key dimensions: heterogeneous support, fine-grained control, and production-grade execution, positioning them within the leading-edge group of industry solutions. In particular, Rise vGPU demonstrates strong performance in compute orchestration and enterprise-grade model management evaluations, achieving a leading position in overall assessment, reflecting its robustness in production deployment. ModelHub, as a critical complementary layer, works in tight integration with vGPU, forming a closed loop across model–hardware compatibility, execution stability and performance consistency, as well as coordinated model–GPU scheduling.
From a system perspective, vGPU addresses today’s challenges—fragmented resources, low utilization, and unstable SLA in heterogeneous environments—by virtualizing and slicing GPUs into a unified, schedulable resource pool. ModelHub, in contrast, targets future requirements, where rapidly expanding model ecosystems, multi-modal workloads, and increasing model diversity require cross-chip portability, automated adaptation, and model-level orchestration. Together, they form a dual-layer evolution path from resource orchestration to model orchestration.
On top of this, 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, upgrading compute from static resources into continuously operable, production-grade infrastructure capability.
如果您对2026年人工智能基础设施编排平台行业有进一步的研究需求，请联系我们：
沙利文 王先生
E-mail: walter.wang@frostchina.com
